Hybrid Network for End-To-End Text-Independent Speaker Identification

Wajdi Ghezaiel, Luc Brun, Olivier Lezoray
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 14:00 in session PS T2.3

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Auto-TLDR; Text-Independent Speaker Identification with Scattering Wavelet Network and Convolutional Neural Networks

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Deep learning has recently improved the performance of Speaker Identification (SI) systems. Promising results have been obtained with Convolutional Neural Networks (CNNs). This success are mostly driven by the advent of large datasets. However in the context of commercial applications, collection of large amount of training data is not always possible. In addition, robustness of a SI system is adversely effected by short utterances. SI with only a few and short utterances is a challenging problem. Therefore, in this paper, we propose a novel text-independent speaker identification system. The proposed system can identify speakers by learning from only few training short utterances examples. To achieve this, we combine CNN with Scattering Wavelet Network. We propose a two-stage feature extraction framework using a two-layer wavelet scattering network coupled with a CNN for SI system. The proposed architecture takes variable length speech segments. To evaluate the effectiveness of the proposed approach, Timit and Librispeech datasets are used in the experiments. These conducted experiments show that our hybrid architecture performs successfully for SI, even with a small number and short duration of training samples. In comparaison with related methods, the obtained results shows that an hybrid architecture achieve better performance.

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DenseRecognition of Spoken Languages

Jaybrata Chakraborty, Bappaditya Chakraborty, Ujjwal Bhattacharya
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; DenseNet: A Dense Convolutional Network Architecture for Speech Recognition in Indian Languages

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In the present study, we have, for the first time, con- sidered a large number of Indian languages for recog- nition from their audio signals of different sources. A dense convolutional network architecture (DenseNet) has been proposed for this classification problem. Dy- namic elimination of low energy frames from the input speech signal has been considered as a preprocessing operation. Mel-spectrogram of pre-processed speech signal is fed to a DenseNet architecture for recogni- tion of its language. Recognition performance of the proposed architecture has been compared with that of several state-of-the-art deep architectures which include a traditional convolutional neural network (CNN), multiple ResNet architectures, CNN-BLSTM and DenseNet-BLSTM hybrid architectures. Addition- ally, we obtained recognition performances of a stacked BLSTM architecture fed with different sets of hand- crafted features for comparison purpose. Simulations have been performed on two different standard datasets which include (i) IITKGP-MLILSC dataset of news clips in 27 different Indian languages and (ii) Linguistic Data Consortium (LDC) dataset of telephonic conver- sations in 5 different Indian languages. Recognition performance of the proposed framework has been found to be consistently and significantly better than all other frameworks implemented in this study.

The Application of Capsule Neural Network Based CNN for Speech Emotion Recognition

Xincheng Wen, Kunhong Liu
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in session PS T5.7

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Auto-TLDR; CapCNN: A Capsule Neural Network for Speech Emotion Recognition

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Moreover, the abstraction of audio features makes it impossible to fully use the inherent relationship among audio features. This paper proposes a model that combines a convolutional neural network(CNN) and a capsule neural network (CapsNet), named as CapCNN. The advantage of CapCNN lies in that it provides a solution to solve time sensitivity and focus on the overall characteristics. In this study, it is found that CapCNN can well handle the speech emotion recognition task. Compared with other state-of-art methods, our algorithm shows high performances on the CASIA and EMODB datasets. The detailed analysis confirms that our method provides balanced results on the various classes.

End-To-End Triplet Loss Based Emotion Embedding System for Speech Emotion Recognition

Puneet Kumar, Sidharth Jain, Balasubramanian Raman, Partha Pratim Roy, Masakazu Iwamura
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; End-to-End Neural Embedding System for Speech Emotion Recognition

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In this paper, an end-to-end neural embedding system based on triplet loss and residual learning has been proposed for speech emotion recognition. The proposed system learns the embeddings from the emotional information of the speech utterances. The learned embeddings are used to recognize the emotions portrayed by given speech samples of various lengths. The proposed system implements Residual Neural Network architecture. It is trained using softmax pre-training and triplet loss function. The weights between the fully connected and embedding layers of the trained network are used to calculate the embedding values. The embedding representations of various emotions are mapped onto a hyperplane, and the angles among them are computed using the cosine similarity. These angles are utilized to classify a new speech sample into its appropriate emotion class. The proposed system has demonstrated 91.67\% and 64.44\% accuracy while recognizing emotions for RAVDESS and IEMOCAP dataset, respectively.

ESResNet: Environmental Sound Classification Based on Visual Domain Models

Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; Environmental Sound Classification with Short-Time Fourier Transform Spectrograms

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Environmental Sound Classification (ESC) is an active research area in the audio domain and has seen a lot of progress in the past years. However, many of the existing approaches achieve high accuracy by relying on domain-specific features and architectures, making it harder to benefit from advances in other fields (e.g., the image domain). Additionally, some of the past successes have been attributed to a discrepancy of how results are evaluated (i.e., on unofficial splits of the UrbanSound8K (US8K) dataset), distorting the overall progression of the field. The contribution of this paper is twofold. First, we present a model that is inherently compatible with mono and stereo sound inputs. Our model is based on simple log-power Short-Time Fourier Transform (STFT) spectrograms and combines them with several well-known approaches from the image domain (i.e., ResNet, Siamese-like networks and attention). We investigate the influence of cross-domain pre-training, architectural changes, and evaluate our model on standard datasets. We find that our model out-performs all previously known approaches in a fair comparison by achieving accuracies of 97.0 % (ESC-10), 91.5 % (ESC-50) and 84.2 % / 85.4 % (US8K mono / stereo). Second, we provide a comprehensive overview of the actual state of the field, by differentiating several previously reported results on the US8K dataset between official or unofficial splits. For better reproducibility, our code (including any re-implementations) is made available.

Which are the factors affecting the performance of audio surveillance systems?

Antonio Greco, Antonio Roberto, Alessia Saggese, Mario Vento
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 14:00 in session OS T5.4

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Auto-TLDR; Sound Event Recognition Using Convolutional Neural Networks and Visual Representations on MIVIA Audio Events

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Sound event recognition systems are rapidly becoming part of our life, since they can be profitably used in several vertical markets, ranging from audio security applications to scene classification and multi-modal analysis in social robotics. In the last years, a not negligible part of the scientific community started to apply Convolutional Neural Networks (CNNs) to image-based representations of the audio stream, due to their successful adoption in almost all the computer vision tasks. In this paper, we carry out a detailed benchmark of various widely used CNN architectures and visual representations on a popular dataset, namely the MIVIA Audio Events database. Our analysis is aimed at understanding how these factors affect the sound event recognition performance with a particular focus on the false positive rate, very relevant in audio surveillance solutions. In fact, although most of the proposed solutions achieve a high recognition rate, the capability of distinguishing the events-of-interest from the background is often not yet sufficient for real systems, and prevent its usage in real applications. Our comprehensive experimental analysis investigates this aspect and allows to identify useful design guidelines for increasing the specificity of sound event recognition systems.

Toward Text-Independent Cross-Lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset

Yi-Chieh Wu, Wen-Hung Liao
Track 2: Biometrics, Human Analysis and Behavior Understanding
Fri 15 Jan 2021 at 15:00 in session PS T2.5

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Auto-TLDR; Cross-lingual Speech for Biometric Recognition

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Over 40% of the world's population is bilingual. Existing speaker identification/verification systems, however, assume the same language type for both enrollment and recognition stages. In this work, we investigate the feasibility of employing multilingual speech for biometric application. We establish a dataset containing audio recorded in English, Mandarin and Taiwanese. Three acoustic features, namely, i-vector, d-vector and x-vector have been evaluated for both speaker verification (SV) and identification (SI) tasks. Preliminary experimental results indicate that x-vector achieves the best overall performance. Additionally, model trained with hybrid data demonstrates highest accuracy associated with the cost of data collection efforts. In SI tasks, we obtained over 91\% cross-lingual accuracy all models using 3-second audio. In SV tasks, the EER among cross-lingual test is at most 6.52\%, which is observed on the model trained by English corpus. The outcome suggests the feasibility of adopting cross-lingual speech in building text-independent speaker recognition systems.

Ballroom Dance Recognition from Audio Recordings

Tomas Pavlin, Jan Cech, Jiri Matas
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; A CNN-based approach to classify ballroom dances given audio recordings

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We propose a CNN-based approach to classify ten genres of ballroom dances given audio recordings, five latin and five standard, namely Cha Cha Cha, Jive, Paso Doble, Rumba, Samba, Quickstep, Slow Foxtrot, Slow Waltz, Tango and Viennese Waltz. We utilize a spectrogram of an audio signal and we treat it as an image that is an input of the CNN. The classification is performed independently by 5-seconds spectrogram segments in sliding window fashion and the results are then aggregated. The method was tested on following datasets: Publicly available Extended Ballroom dataset collected by Marchand and Peeters, 2016 and two YouTube datasets collected by us, one in studio quality and the other, more challenging, recorded on mobile phones. The method achieved accuracy 93.9%, 96.7% and 89.8% respectively. The method runs in real-time. We implemented a web application to demonstrate the proposed method.

Detection of Calls from Smart Speaker Devices

Vinay Maddali, David Looney, Kailash Patil
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Distinguishing Between Smart Speaker and Cell Devices Using Only the Audio Using a Feature Set

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The ubiquity of smart speakers is increasing, with a growing number of households utilising these devices to make calls over the telephony network. As the technology is typically configured to retain the cellular phone number of the user, it presents challenges in applications where knowledge of the true call origin is required. There are a wide range of makes and models for these devices, as is the case with cell phones, and it is challenging to detect the general category as a smart speaker or cell, independent of the designated phone number. In this paper, we present an approach to differentiate between calls originating from smart speakers and ones from cellular devices using only the audio. We present a feature set that characterises the relevant acoustic information, such as the degree of reverberation and noise, to distinguish between these categories. When evaluated on a dataset spanning multiple models for each device category, as well as different modes-of-usage and microphone-speaker distances, the method yields an Equal Error Rate (EER) of 12.6%.

Digit Recognition Applied to Reconstructed Audio Signals Using Deep Learning

Anastasia-Sotiria Toufa, Constantine Kotropoulos
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Compressed Sensing for Digit Recognition in Audio Reconstruction

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Compressed sensing allows signal reconstruction from a few measurements. This work proposes a complete pipeline for digit recognition applied to audio reconstructed signals. The reconstruction procedure exploits the assumption that the original signal lies in the range of a generator. A pretrained generator of a Generative Adversarial Network generates audio digits. A new method for reconstruction is proposed, using only the most active segment of the signal, i.e., the segment with the highest energy. The underlying assumption is that such segment offers a more compact representation, preserving the meaningful content of signal. Cases when the reconstruction produces noise, instead of digit, are treated as outliers. In order to detect and reject them, three unsupervised indicators are used, namely, the total energy of reconstructed signal, the predictions of an one-class Support Vector Machine, and the confidence of a pretrained classifier used for recognition. This classifier is based on neural networks architectures and is pretrained on original audio recordings, employing three input representations, i.e., raw audio, spectrogram, and gammatonegram. Experiments are conducted, analyzing both the quality of reconstruction and the performance of classifiers in digit recognition, demonstrating that the proposed method yields higher performance in both the quality of reconstruction and digit recognition accuracy.

Audio-Visual Speech Recognition Using a Two-Step Feature Fusion Strategy

Hong Liu, Wanlu Xu, Bing Yang
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 12:00 in session PS T1.10

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Auto-TLDR; A Two-Step Feature Fusion Network for Speech Recognition

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Lip-reading methods and fusion strategy are crucial for audio-visual speech recognition. In recent years, most approaches involve two separate audio and visual streams with early or late fusion strategies. Such a single-stage fusion method may fail to guarantee the integrity and representativeness of fusion information simultaneously. This paper extends a traditional single-stage fusion network to a two-step feature fusion network by adding an audio-visual early feature fusion (AV-EFF) stream to the baseline model. This method can learn the fusion information of different stages, preserving the original features as much as possible and ensuring the independence of different features. Besides, to capture long-range dependencies of video information, a non-local block is added to the feature extraction part of the visual stream (NL-Visual) to obtain the long-term spatio-temporal features. Experimental results on the two largest public datasets in English (LRW) and Mandarin (LRW-1000) demonstrate our method is superior to other state-of-the-art methods.

The Effect of Spectrogram Reconstruction on Automatic Music Transcription: An Alternative Approach to Improve Transcription Accuracy

Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos, Herremans Dorien
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 14:00 in session OS T5.4

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Auto-TLDR; Exploring the effect of spectrogram reconstruction loss on automatic music transcription

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Most of the state-of-the-art automatic music transcription (AMT) models break down the main transcription task into sub-tasks such as onset prediction and offset prediction and train them with onset and offset labels. These predictions are then concatenated together and used as the input to train another model with the pitch labels to obtain the final transcription. We attempt to use only the pitch labels (together with spectrogram reconstruction loss) and explore how far this model can go without introducing supervised sub-tasks. In this paper, we do not aim at achieving state-of-the-art transcription accuracy, instead, we explore the effect that spectrogram reconstruction has on our AMT model. Our proposed model consists of two U-nets: the first U-net transcribes the spectrogram into a posteriorgram, and a second U-net transforms the posteriorgram back into a spectrogram. A reconstruction loss is applied between the original spectrogram and the reconstructed spectrogram to constrain the second U-net to focus only on reconstruction. We train our model on different datasets including MAPS, MAESTRO, and MusicNet. Our experiments show that adding the reconstruction loss can generally improve the note-level transcription accuracy when compared to the same model without the reconstruction part. Moreover, it can also boost the frame-level precision to be higher than the state-of-the-art models. The feature maps learned by our u-net contain gridlike structures (not present in the baseline model) which implies that with the present of reconstruction loss, the model is probably trying to count along both the time and frequency axis, resulting in a higher note-level transcription accuracy.

Audio-Video Detection of the Active Speaker in Meetings

Francisco Madrigal, Frederic Lerasle, Lionel Pibre, Isabelle Ferrané
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 14:00 in session PS T2.3

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Auto-TLDR; Active Speaker Detection with Visual and Contextual Information from Meeting Context

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Meetings are a common activity that provides certain challenges when creating systems that assist them. Such is the case of the Active speaker detection, which can provide useful information for human interaction modeling, or human-robot interaction. Active speaker detection is mostly done using speech, however, certain visual and contextual information can provide additional insights. In this paper we propose an active speaker detection framework that integrates audiovisual features with social information, from the meeting context. Visual cue is processed using a Convolutional Neural Network (CNN) that captures the spatio-temporal relationships. We analyze several CNN architectures with both cues: raw pixels (RGB images) and motion (estimated with optical flow). Contextual reasoning is done with an original methodology, based on the gaze of all participants. We evaluate our proposal with a public \textcolor{black}{benchmark} in state-of-art: AMI corpus. We show how the addition of visual and context information improves the performance of the active speaker detection.

Audio-Based Near-Duplicate Video Retrieval with Audio Similarity Learning

Pavlos Avgoustinakis, Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Andreas L. Symeonidis, Ioannis Kompatsiaris
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in session PS T5.7

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Auto-TLDR; AuSiL: Audio Similarity Learning for Near-duplicate Video Retrieval

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In this work, we address the problem of audio-based near-duplicate video retrieval. We propose the Audio Similarity Learning (AuSiL) approach that effectively captures temporal patterns of audio similarity between video pairs. For the robust similarity calculation between two videos, we first extract representative audio-based video descriptors by leveraging transfer learning based on a Convolutional Neural Network (CNN) trained on a large scale dataset of audio events, and then we calculate the similarity matrix derived from the pairwise similarity of these descriptors. The similarity matrix is subsequently fed to a CNN network that captures the temporal structures existing within its content. We train our network following a triplet generation process and optimizing the triplet loss function. To evaluate the effectiveness of the proposed approach, we have manually annotated two publicly available video datasets based on the audio duplicity between their videos. The proposed approach achieves very competitive results compared to three state-of-the-art methods. Also, unlike the competing methods, it is very robust for the retrieval of audio duplicates generated with speed transformations.

Mood Detection Analyzing Lyrics and Audio Signal Based on Deep Learning Architectures

Konstantinos Pyrovolakis, Paraskevi Tzouveli, Giorgos Stamou
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Automated Music Mood Detection using Music Information Retrieval

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Digital era has changed the way music is produced and propagated creating new needs for automated and more effective management of music tracks in big volumes. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval) and connected with many research papers in the past few years. In order to approach the task of mood detection, we faced separately the analysis of musical lyrics and the analysis of musical audio signal. Then we applied a uniform multichannel analysis to classify our data in mood classes. The available data we will use to train and evaluate our models consists of a total of 2.000 song titles, classified in four mood classes {happy, angry, sad, relaxed}. The result of this process leads to a uniform prediction for emotional arousal that a music track can cause to a listener and show the way to develop many applications.

One-Shot Learning for Acoustic Identification of Bird Species in Non-Stationary Environments

Michelangelo Acconcjaioco, Stavros Ntalampiras
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 16:30 in session PS T5.4

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Auto-TLDR; One-shot Learning in the Bioacoustics Domain using Siamese Neural Networks

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This work introduces the one-shot learning paradigm in the computational bioacoustics domain. Even though, most of the related literature assumes availability of data characterizing the entire class dictionary of the problem at hand, that is rarely true as a habitat's species composition is only known up to a certain extent. Thus, the problem needs to be addressed by methodologies able to cope with non-stationarity. To this end, we propose a framework able to detect changes in the class dictionary and incorporate new classes on the fly. We design an one-shot learning architecture composed of a Siamese Neural Network operating in the logMel spectrogram space. We extensively examine the proposed approach on two datasets of various bird species using suitable figures of merit. Interestingly, such a learning scheme exhibits state of the art performance, while taking into account extreme non-stationarity cases.

Feature Engineering and Stacked Echo State Networks for Musical Onset Detection

Peter Steiner, Azarakhsh Jalalvand, Simon Stone, Peter Birkholz
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; Echo State Networks for Onset Detection in Music Analysis

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In music analysis, one of the most fundamental tasks is note onset detection - detecting the beginning of new note events. As the target function of onset detection is related to other tasks, such as beat tracking or tempo estimation, onset detection is the basis for such related tasks. Furthermore, it can help to improve Automatic Music Transcription (AMT). Typically, different approaches for onset detection follow a similar outline: An audio signal is transformed into an Onset Detection Function (ODF), which should have rather low values (i.e. close to zero) for most of the time but with pronounced peaks at onset times, which can then be extracted by applying peak picking algorithms on the ODF. In the recent years, several kinds of neural networks were used successfully to compute the ODF from feature vectors. Currently, Convolutional Neural Networks (CNNs) define the state of the art. In this paper, we build up on an alternative approach to obtain a ODF by Echo State Networks (ESNs), which have achieved comparable results to CNNs in several tasks, such as speech and image recognition. In contrast to the typical iterative training procedures of deep learning architectures, such as CNNs or networks consisting of Long-Short-Term Memory Cells (LSTMs), in ESNs only a very small part of the weights is easily trained in one shot using linear regression. By comparing the performance of several feature extraction methods, pre-processing steps and introducing a new way to stack ESNs, we expand our previous approach to achieve results that fall between a bidirectional LSTM network and a CNN with relative improvements of 1.8% and -1.4%, respectively. For the evaluation, we used exactly the same 8-fold cross validation setup as for the reference results.

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Michele Alberti, Angela Botros, Schuetz Narayan, Rolf Ingold, Marcus Liwicki, Mathias Seuret
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 15:00 in session PS T1.2

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Auto-TLDR; Trainable and Spectrally Initializable Matrix Transformations for Neural Networks

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In this work, we introduce a new architectural component to Neural Networks (NN), i.e., trainable and spectrally initializable matrix transformations on feature maps. While previous literature has already demonstrated the possibility of adding static spectral transformations as feature processors, our focus is on more general trainable transforms. We study the transforms in various architectural configurations on four datasets of different nature: from medical (ColorectalHist, HAM10000) and natural (Flowers) images to historical documents (CB55). With rigorous experiments that control for the number of parameters and randomness, we show that networks utilizing the introduced matrix transformations outperform vanilla neural networks. The observed accuracy increases appreciably across all datasets. In addition, we show that the benefit of spectral initialization leads to significantly faster convergence, as opposed to randomly initialized matrix transformations. The transformations are implemented as auto-differentiable PyTorch modules that can be incorporated into any neural network architecture. The entire code base is open-source.

Improving Gravitational Wave Detection with 2D Convolutional Neural Networks

Siyu Fan, Yisen Wang, Yuan Luo, Alexander Michael Schmitt, Shenghua Yu
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Two-dimensional Convolutional Neural Networks for Gravitational Wave Detection from Time Series with Background Noise

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Sensitive gravitational wave (GW) detectors such as that of Laser Interferometer Gravitational-wave Observatory (LIGO) realize the direct observation of GW signals that confirm Einstein's general theory of relativity. However, it remains challenges to quickly detect faint GW signals from a large number of time series with background noise under unknown probability distributions. Traditional methods such as matched-filtering in general assume Additive White Gaussian Noise (AWGN) and are far from being real-time due to its high computational complexity. To avoid these weaknesses, one-dimensional (1D) Convolutional Neural Networks (CNNs) are introduced to achieve fast online detection in milliseconds but do not have enough consideration on the trade-off between the frequency and time features, which will be revisited in this paper through data pre-processing and subsequent two-dimensional (2D) CNNs during offline training to improve the online detection sensitivity. In this work, the input data is pre-processed to form a 2D spectrum by Short-time Fourier transform (STFT), where frequency features are extracted without learning. Then, carrying out two 1D convolutions across time and frequency axes respectively, and concatenating the time-amplitude and frequency-amplitude feature maps with equal proportion subsequently, the frequency and time features are treated equally as the input of our following two-dimensional CNNs. The simulation of our above ideas works on a generated data set with uniformly varying SNR (2-17), which combines the GW signal generated by PYCBC and the background noise sampled directly from LIGO. Satisfying the real-time online detection requirement without noise distribution assumption, the experiments of this paper demonstrate better performance in average compared to that of 1D CNNs, especially in the cases of lower SNR (4-9).

Exploring Spatial-Temporal Representations for fNIRS-based Intimacy Detection via an Attention-enhanced Cascade Convolutional Recurrent Neural Network

Chao Li, Qian Zhang, Ziping Zhao
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; Intimate Relationship Prediction by Attention-enhanced Cascade Convolutional Recurrent Neural Network Using Functional Near-Infrared Spectroscopy

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The detection of intimacy plays a crucial role in the improvement of intimate relationship, which contributes to promote the family and social harmony. Previous studies have shown that different degrees of intimacy have significant differences in brain imaging. Recently, a few of work has emerged to recognise intimacy automatically by using machine learning technique. Moreover, considering the temporal dynamic characteristics of intimacy relationship on neural mechanism, how to model spatio-temporal dynamics for intimacy prediction effectively is still a challenge. In this paper, we propose a novel method to explore deep spatial-temporal representations for intimacy prediction by Attention-enhanced Cascade Convolutional Recurrent Neural Network (ACCRNN). Given the advantages of time-frequency resolution in complex neuronal activities analysis, this paper utilizes functional near-infrared spectroscopy (fNIRS) to analyse and infer to intimate relationship. We collect a fNIRS-based dataset for the analysis of intimate relationship. Forty-two-channel fNIRS signals are recorded from the 44 subjects' prefrontal cortex when they watched a total of 18 photos of lovers, friends and strangers for 30 seconds per photo. The experimental results show that our proposed method outperforms the others in terms of accuracy with the precision of 96.5%. To the best of our knowledge, this is the first time that such a hybrid deep architecture has been employed for fNIRS-based intimacy prediction.

Improving Mix-And-Separate Training in Audio-Visual Sound Source Separation with an Object Prior

Quan Nguyen, Simone Frintrop, Timo Gerkmann, Mikko Lauri, Julius Richter
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 14:00 in session OS T5.4

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Auto-TLDR; Object-Prior: Learning the 1-to-1 correspondence between visual and audio signals by audio- visual sound source methods

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The performance of an audio-visual sound source separation system is determined by its ability to separate audio sources given images of the sources and the audio mixture. The goal of this study is to investigate the ability to learn the mapping between the sounds and the images of instruments by audio- visual sound source separation methods based on the state-of-the- art PixelPlayer [1]. Theoretical and empirical analyses illustrate that the PixelPlayer is not properly trained to learn the 1-to- 1 correspondence between visual and audio signals during its mix-and-separate training process. Based on the insights from this analysis, a weakly-supervised method called Object-Prior is proposed and evaluated on two audio-visual datasets. The experimental results show that the proposed Object-Prior method outperforms the PixelPlayer and other baselines in the audio- visual sound source separation task. It is also more robust against asynchronized data, where the frame and the audio do not come from the same video, and recognizes musical instruments based on their sound with higher accuracy than the PixelPlayer. This indicates that learning the 1-to-1 correspondence between visual and audio features of an instrument improves the effectiveness of audio-visual sound source separation.

AttendAffectNet: Self-Attention Based Networks for Predicting Affective Responses from Movies

Thi Phuong Thao Ha, Bt Balamurali, Herremans Dorien, Roig Gemma
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; AttendAffectNet: A Self-Attention Based Network for Emotion Prediction from Movies

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In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet. We take both audio and video into account and incorporate the relation among multiple modalities by applying self-attention mechanism in a novel manner into the extracted features for emotion prediction. We compare it to the typically temporal integration of the self-attention based model, which in our case, allows to capture the relation of temporal representations of the movie while considering the sequential dependencies of emotion responses. We demonstrate the effectiveness of our proposed architectures on the extended COGNIMUSE dataset [1], [2] and the MediaEval 2016 Emotional Impact of Movies Task [3], which consist of movies with emotion annotations. Our results show that applying the self-attention mechanism on the different audio-visual features, rather than in the time domain, is more effective for emotion prediction. Our approach is also proven to outperform state-of-the-art models for emotion prediction.

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Jianrong Wang, Tong Wu, Shanyu Wang, Mei Yu, Qiang Fang, Ju Zhang, Li Liu
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 12:00 in session PS T2.2

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Auto-TLDR; Lip Motion Network for Text-Independent and Text-Dependent Speaker Recognition

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Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two dimensional (2D) lip images to recognize speaker in a text-dependent context. However, 2D lip easily suffers from face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A novel regional feedback module (RFM) is proposed to explore attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LMNet is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip-Motion-Ne twork-for-Text-Independent-Speaker-Recognition.

Influence of Event Duration on Automatic Wheeze Classification

Bruno M Rocha, Diogo Pessoa, Alda Marques, Paulo Carvalho, Rui Pedro Paiva
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 16:00 in session PS T1.15

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Auto-TLDR; Experimental Design of the Non-wheeze Class for Wheeze Classification

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Patients with respiratory conditions typically exhibit adventitious respiratory sounds, such as wheezes. Wheeze events have variable duration. In this work we studied the influence of event duration on wheeze classification, namely how the creation of the non-wheeze class affected the classifiers' performance. First, we evaluated several classifiers on an open access respiratory sound database, with the best one reaching sensitivity and specificity values of 98% and 95%, respectively. Then, by changing one parameter in the design of the non-wheeze class, i.e., event duration, the best classifier only reached sensitivity and specificity values of 53% and 75%, respectively. These results demonstrate the importance of experimental design on the assessment of wheeze classification algorithms' performance.

EasiECG: A Novel Inter-Patient Arrhythmia Classification Method Using ECG Waves

Chuanqi Han, Ruoran Huang, Fang Yu, Xi Huang, Li Cui
Track 5: Image and Signal Processing
Wed 13 Jan 2021 at 12:00 in session PS T5.3

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Auto-TLDR; EasiECG: Attention-based Convolution Factorization Machines for Arrhythmia Classification

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Abstract—In an ECG record, the PQRST waves are of important medical significance which provide ample information reflecting heartbeat activities. In this paper, we propose a novel arrhythmia classification method namely EasiECG, characterized by simplicity and accuracy. Compared with other works, the EasiECG takes the configuration of these five key waves into account and does not require complicated feature engineering. Meanwhile, an additional encoding of the extracted features makes the EasiECG applicable even on samples with missing waves. To automatically capture interactions that contribute to the classification among the processed features, a novel adapted classification model named Attention-based Convolution Factorization Machines (ACFM) is proposed. In detail, the ACFM can learn both linear and high-order interactions from linear regression and convolution on outer-product feature interaction maps, respectively. After that, an attention mechanism implemented in the model can further assign different importance of these interactions when predicting certain types of heartbeats. To validate the effectiveness and practicability of our EasiECG, extensive experiments of inter-patient paradigm on the benchmark MIT-BIH arrhythmia database are conducted. To tackle the imbalanced sample problem in this dataset, an ingenious loss function: focal loss is adopted when training. The experiment results show that our method is competitive compared with other state-of-the-arts, especially in classifying the Supraventricular ectopic beats. Besides, the EasiECG achieves an overall accuracy of 87.6% on samples with a missing wave in the related experiment, demonstrating the robustness of our proposed method.

Exploring Seismocardiogram Biometrics with Wavelet Transform

Po-Ya Hsu, Po-Han Hsu, Hsin-Li Liu
Track 2: Biometrics, Human Analysis and Behavior Understanding
Tue 12 Jan 2021 at 17:00 in session PS T2.1

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Auto-TLDR; Seismocardiogram Biometric Matching Using Wavelet Transform and Deep Learning Models

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Seismocardiogram (SCG) has become easily accessible in the past decade owing to the advance of sensor technology. However, SCG biometric has not been widely explored. In this paper, we propose combining wavelet transform together with deep learning models, machine learning classifiers, or structural similarity metric to perform SCG biometric matching tasks. We validate the proposed methods on the publicly available dataset from PhysioNet database. The dataset contains one hour long electrocardiogram, breathing, and SCG data of 20 subjects. We train the models on the first five minute SCG and conduct identification on the last five minute SCG. We evaluate the identification and authentication performance with recognition rate and equal error rate, respectively. Based on the results, we show that wavelet transformed SCG biometric can achieve state-of-the-art performance when combined with deep learning models, machine learning classifiers, or structural similarity.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Stéphane Lathuiliere, Pablo Mesejo, Radu Horaud
Track 2: Biometrics, Human Analysis and Behavior Understanding
Tue 12 Jan 2021 at 14:00 in session OS T2.1

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Auto-TLDR; Deep Visual Voice Activity Detection with Optical Flow

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Visual voice activity detection (V-VAD) uses visual features to predict whether a person is speaking or not. V-VAD is useful whenever audio VAD (A-VAD) is inefficient either because the acoustic signal is difficult to analyze or is simply missing. We propose two deep architectures for V-VAD, one based on facial landmarks and one based on optical flow. Moreover, available datasets, used for learning and for testing V-VAD, lack content variability. We introduce a novel methodology to automatically create and annotate very large datasets in-the-wild, based on combining A-VAD and face detection. A thorough empirical evaluation shows the advantage of training the proposed deep V-VAD models with such a dataset.

Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

Yongqiang Dou, Haocheng Yang, Maolin Yang, Yanyan Xu, Dengfeng Ke
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; Anti-Spoofing with Balanced Focal Loss Function and Combination Features

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It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. Therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.

Spatial Bias in Vision-Based Voice Activity Detection

Kalin Stefanov, Mohammad Adiban, Giampiero Salvi
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Fri 15 Jan 2021 at 16:00 in session PS T1.16

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Auto-TLDR; Spatial Bias in Vision-based Voice Activity Detection in Multiparty Human-Human Interactions

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We present models for automatic vision-based voice activity detection (VAD) in multiparty human-human interactions that are aimed at complementing the acoustic VAD methods. We provide evidence that this type of vision-based VAD models are susceptible to spatial bias in the datasets. The physical settings of the interaction, usually constant throughout data acquisition, determines the distribution of head poses of the participants. Our results show that when the head pose distributions are significantly different in the training and test sets, the performance of the models drops significantly. This suggests that previously reported results on datasets with a fixed physical configuration may overestimate the generalization capabilities of this type of models. We also propose a number of possible remedies to the spatial bias, including data augmentation, input masking and dynamic features, and provide an in-depth analysis of the visual cues used by our models.

Mutual Alignment between Audiovisual Features for End-To-End Audiovisual Speech Recognition

Hong Liu, Yawei Wang, Bing Yang
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 16:00 in session PS T5.6

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Auto-TLDR; Mutual Iterative Attention for Audio Visual Speech Recognition

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Asynchronization issue caused by different types of modalities is one of the major problems in audio visual speech recognition (AVSR) research. However, most AVSR systems merely rely on up sampling of video or down sampling of audio to align audio and visual features, assuming that the feature sequences are aligned frame-by-frame. These pre-processing steps oversimplify the asynchrony relation between acoustic signal and lip motion, lacking flexibility and impairing the performance of the system. Although there are systems modeling the asynchrony between the modalities, sometimes they fail to align speech and video precisely in some even all noise conditions. In this paper, we propose a mutual feature alignment method for AVSR which can make full use of cross modility information to address the asynchronization issue by introducing Mutual Iterative Attention (MIA) mechanism. Our method can automatically learn an alignment in a mutual way by performing mutual attention iteratively between the audio and visual features, relying on the modified encoder structure of Transformer. Experimental results show that our proposed method obtains absolute improvements up to 20.42% over the audio modality alone depending upon the signal-to-noise-ratio (SNR) level. Better recognition performance can also be achieved comparing with the traditional feature concatenation method under both clean and noisy conditions. It is expectable that our proposed mutual feature alignment method can be easily generalized to other multimodal tasks with semantically correlated information.

Cross-Lingual Text Image Recognition Via Multi-Task Sequence to Sequence Learning

Zhuo Chen, Fei Yin, Xu-Yao Zhang, Qing Yang, Cheng-Lin Liu
Track 4: Document and Media Analysis
Wed 13 Jan 2021 at 12:00 in session PS T4.2

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Auto-TLDR; Cross-Lingual Text Image Recognition with Multi-task Learning

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This paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image Recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image Recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a BLSTM model with CTC, are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.

ResMax: Detecting Voice Spoofing Attacks with Residual Network and Max Feature Map

Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Jun Ho Huh, Choong-Hoon Lee, Youngbae Jeon, Jeonghwan Hwang, Ji Won Yoon
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; ASVspoof 2019: A Lightweight Automatic Speaker Verification Spoofing and Countermeasures System

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The ``2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge'' (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy -- this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019 dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, outperforming the top ensemble system from the competition that achieved an EER of 0.39%.

Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Nikolaos Bakalos
Track 2: Biometrics, Human Analysis and Behavior Understanding
Thu 14 Jan 2021 at 12:00 in session PS T2.4

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Auto-TLDR; Tensor-Based Neural Network for Spatiotemporal Pose Classifiaction using Three-Dimensional Skeleton Data

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Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classifiaction using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.

Graph Convolutional Neural Networks for Power Line Outage Identification

Jia He, Maggie Cheng
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 14:00 in session PS T1.6

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Auto-TLDR; Graph Convolutional Networks for Power Line Outage Identification

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In this paper, we consider the power line outage identification problem as a graph signal classification problem, where the signal at each vertex is given as a time series. We propose graph convolutional networks (GCNs) for the task of classifying signals supported on graphs. An important element of the GCN design is filter design. We consider filtering signals in either the vertex (spatial) domain, or the frequency (spectral) domain. Two basic architectures are proposed. In the spatial GCN architecture, the GCN uses a graph shift operator as the basic building block to incorporate the underlying graph structure into the convolution layer. The spatial filter directly utilizes the graph connectivity information. It defines the filter to be a polynomial in the graph shift operator to obtain the convolved features that aggregate neighborhood information of each node. In the spectral GCN architecture, a frequency filter is used instead. A graph Fourier transform operator first transforms the raw graph signal from the vertex domain to the frequency domain, and then a filter is defined using the graph's spectral parameters. The spectral GCN then uses the output from the graph Fourier transform to compute the convolved features. There are additional challenges to classify the time-evolving graph signal as the signal value at each vertex changes over time. The GCNs are designed to recognize different spatiotemporal patterns from high-dimensional data defined on a graph. The application of the proposed methods to power line outage identification shows that these GCN architectures can successfully classify abnormal signal patterns and identify the outage location.

Adversarially Training for Audio Classifiers

Raymel Alfonso Sallo, Mohammad Esmaeilpour, Patrick Cardinal
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.8

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Auto-TLDR; Adversarially Training for Robust Neural Networks against Adversarial Attacks

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In this paper, we investigate the potential effect of the adversarially training on the robustness of six advanced deep neural networks against a variety of targeted and non-targeted adversarial attacks. We firstly show that, the ResNet-56 model trained on the 2D representation of the discrete wavelet transform appended with the tonnetz chromagram outperforms other models in terms of recognition accuracy. Then we demonstrate the positive impact of adversarially training on this model as well as other deep architectures against six types of attack algorithms (white and black-box) with the cost of the reduced recognition accuracy and limited adversarial perturbation. We run our experiments on two benchmarking environmental sound datasets and show that without any imposed limitations on the budget allocations for the adversary, the fooling rate of the adversarially trained models can exceed 90%. In other words, adversarial attacks exist in any scales, but they might require higher adversarial perturbations compared to non-adversarially trained models.

Generalization Comparison of Deep Neural Networks Via Output Sensitivity

Mahsa Forouzesh, Farnood Salehi, Patrick Thiran
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Tue 12 Jan 2021 at 14:00 in session OS T1.1

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Auto-TLDR; Generalization of Deep Neural Networks using Sensitivity

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Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the loss function to the output's sensitivity to its input. We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing the generalization performance of networks, without requiring labeled data. We find that sensitivity is decreased by applying popular methods which improve the generalization performance of the model, such as (1) using a deep network rather than a wide one, (2) adding convolutional layers to baseline classifiers instead of adding fully-connected layers, (3) using batch normalization, dropout and max-pooling, and (4) applying parameter initialization techniques.

Radar Image Reconstruction from Raw ADC Data Using Parametric Variational Autoencoder with Domain Adaptation

Michael Stephan, Thomas Stadelmayer, Avik Santra, Georg Fischer, Robert Weigel, Fabian Lurz
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Parametric Variational Autoencoder-based Human Target Detection and Localization for Frequency Modulated Continuous Wave Radar

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This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.

Person Recognition with HGR Maximal Correlation on Multimodal Data

Yihua Liang, Fei Ma, Yang Li, Shao-Lun Huang
Track 5: Image and Signal Processing
Thu 14 Jan 2021 at 12:00 in session PS T5.5

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Auto-TLDR; A correlation-based multimodal person recognition framework that learns discriminative embeddings of persons by joint learning visual features and audio features

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Multimodal person recognition is a common task in video analysis and public surveillance, where information from multiple modalities, such as images and audio extracted from videos, are used to jointly determine the identity of a person. Previous person recognition techniques either use only uni-modal data or only consider shared representations between different input modalities, while leaving the extraction of their relationship with identity information to downstream tasks. Furthermore, real-world data often contain noise, which makes recognition more challenging practical situations. In our work, we propose a novel correlation-based multimodal person recognition framework that is relatively simple but can efficaciously learn supervised information in multimodal data fusion and resist noise. Specifically, our framework learns a discriminative embeddings of persons by joint learning visual features and audio features while maximizing HGR maximal correlation among multimodal input and persons' identities. Experiments are done on a subset of Voxceleb2. Compared with state-of-the-art methods, the proposed method demonstrates an improvement of accuracy and robustness to noise.

Electroencephalography Signal Processing Based on Textural Features for Monitoring the Driver’s State by a Brain-Computer Interface

Giulia Orrù, Marco Micheletto, Fabio Terranova, Gian Luca Marcialis
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 14:00 in session PS T2.3

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Auto-TLDR; One-dimensional Local Binary Pattern Algorithm for Estimating Driver Vigilance in a Brain-Computer Interface System

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In this study we investigate a textural processing method of electroencephalography (EEG) signal as an indicator to estimate the driver's vigilance in a hypothetical Brain-Computer Interface (BCI) system. The novelty of the solution proposed relies on employing the one-dimensional Local Binary Pattern (1D-LBP) algorithm for feature extraction from pre-processed EEG data. From the resulting feature vector, the classification is done according to three vigilance classes: awake, tired and drowsy. The claim is that the class transitions can be detected by describing the variations of the micro-patterns' occurrences along the EEG signal. The 1D-LBP is able to describe them by detecting mutual variations of the signal temporarily "close" as a short bit-code. Our analysis allows to conclude that the 1D-LBP adoption has led to significant performance improvement. Moreover, capturing the class transitions from the EEG signal is effective, although the overall performance is not yet good enough to develop a BCI for assessing the driver's vigilance in real environments.

Automatic Annotation of Corpora for Emotion Recognition through Facial Expressions Analysis

Alex Mircoli, Claudia Diamantini, Domenico Potena, Emanuele Storti
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 16:00 in session PS T5.8

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Auto-TLDR; Automatic annotation of video subtitles on the basis of facial expressions using machine learning algorithms

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The recent diffusion of social networks has made available an unprecedented amount of user-generated content, which may be analyzed in order to determine people's opinions and emotions about a large variety of topics. Research has made many efforts in defining accurate algorithms for analyzing emotions expressed by users in texts; however, their performance often rely on the existence of large annotated datasets, whose current scarcity represents a major issue. The manual creation of such datasets represents a costly and time-consuming activity and hence there is an increasing demand for techniques for the automatic annotation of corpora. In this work we present a methodology for the automatic annotation of video subtitles on the basis of the analysis of facial expressions of people in videos, with the goal of creating annotated corpora that may be used to train emotion recognition algorithms. Facial expressions are analyzed through machine learning algorithms, on the basis of a set of manually-engineered facial features that are extracted from video frames. The soundness of the proposed methodology has been evaluated through an extensive experimentation aimed at determining the performance on real datasets of each methodological step.

Robust Audio-Visual Speech Recognition Based on Hybrid Fusion

Hong Liu, Wenhao Li, Bing Yang
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.2

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Auto-TLDR; Hybrid Fusion Based AVSR with Residual Networks and Bidirectional Gated Recurrent Unit for Robust Speech Recognition in Noise Conditions

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The fusion of audio and visual modalities is an important stage of audio-visual speech recognition (AVSR), which is generally approached through feature fusion or decision fusion. Feature fusion can exploit the covariations between features from different modalities effectively, whereas decision fusion shows the robustness of capturing an optimal combination of multi-modality. In this work, to take full advantage of the complementarity of the two fusion strategies and address the challenge of inherent ambiguity in noisy environments, we propose a novel hybrid fusion based AVSR method with residual networks and Bidirectional Gated Recurrent Unit (BGRU), which is able to distinguish homophones in both clean and noisy conditions. Specifically, a simple yet effective audio-visual encoder is used to map audio and visual features into a shared latent space to capture more discriminative multi-modal feature and find the internal correlation between spatial-temporal information for different modalities. Furthermore, a decision fusion module is designed to get final predictions in order to robustly utilize the reliability measures of audio-visual information. Finally, we introduce a combined loss, which shows its noise-robustness in learning the joint representation across various modalities. Experimental results on the largest publicly available dataset (LRW) demonstrate the robustness of the proposed method under various noisy conditions.

Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture

Ramy Hussein, Rabab K. Ward, Soojin Lee, Martin Mckeown
Track 5: Image and Signal Processing
Tue 12 Jan 2021 at 17:00 in session PS T5.1

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Auto-TLDR; Semi-Dilated Convolutional Network for Seizure Prediction using EEG Scalograms

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Despite many recent advances in machine learning and time-series classification, accurate prediction of seizures remains elusive. In this work, we develop a convolutional network module that uses Electroencephalogram (EEG) scalograms to distinguish between the pre-seizure and normal brain activities. Since the EEG scalogram takes rectangular image format with many more temporal bins than spectral bins, the presented module uses "semi-dilated convolutions" to also create a proportional non-square receptive field. The proposed semi-dilated convolutions support exponential expansion of the receptive field over the long dimension (image width, i.e. time) while maintaining high resolution over the short dimension (image height, i.e., frequency). The proposed architecture comprises a set of co-operative semi-dilated convolutional blocks, each block has a stack of parallel semi-dilated convolutional modules with different dilation rates. Results show that our proposed seizure prediction solution outperforms the state-of-the-art methods, achieving a seizure prediction sensitivity of 88.45% and 89.52% for the American Epilepsy Society and Melbourne University EEG datasets, respectively.

Kernel-based Graph Convolutional Networks

Hichem Sahbi
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Wed 13 Jan 2021 at 16:30 in session PS T1.7

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Auto-TLDR; Spatial Graph Convolutional Networks in Recurrent Kernel Hilbert Space

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Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains. Most of the existing GCNs follow a neighborhood aggregation scheme, where the representation of a node is recursively obtained by aggregating its neighboring node representations using averaging or sorting operations. However, these operations are either ill-posed or weak to be discriminant or increase the number of training parameters and thereby the computational complexity and the risk of overfitting. In this paper, we introduce a novel GCN framework that achieves spatial graph convolution in a reproducing kernel Hilbert space. The latter makes it possible to design, via implicit kernel representations, convolutional graph filters in a high dimensional and more discriminating space without increasing the number of training parameters. The particularity of our GCN model also resides in its ability to achieve convolutions without explicitly realigning nodes in the receptive fields of the learned graph filters with those of the input graphs, thereby making convolutions permutation agnostic and well defined. Experiments conducted on the challenging task of skeleton-based action recognition show the superiority of the proposed method against different baselines as well as the related work.

Wireless Localisation in WiFi Using Novel Deep Architectures

Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham
Track 1: Artificial Intelligence, Machine Learning for Pattern Analysis
Thu 14 Jan 2021 at 14:00 in session PS T1.11

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Auto-TLDR; Deep Neural Network for Indoor Localisation of WiFi Devices in Indoor Environments

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This paper studies the indoor localisation of WiFi devices based on a commodity chipset and standard channel sounding. First, we present a novel shallow neural network (SNN) in which features are extracted from the channel state information (CSI) corresponding to WiFi subcarriers received on different antennas and used to train the model. The single layer architecture of this localisation neural network makes it lightweight and easy-to-deploy on devices with stringent constraints on computational resources. We further investigate for localisation the use of deep learning models and design novel architectures for convolutional neural network (CNN) and long-short term memory (LSTM). We extensively evaluate these localisation algorithms for continuous tracking in indoor environments. Experimental results prove that even an SNN model, after a careful handcrafted feature extraction, can achieve accurate localisation. Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates. We also found that the performance of neural network-based methods are directly affected by the number of anchor access points (APs) regardless of their structure. With three APs, all neural network models proposed in this paper can obtain localisation accuracy of around 0.5 metres. In addition the proposed deep NN architecture reduces the data pre-processing time by 6.5 hours compared with a shallow NN using the data collected in our testbed. In the deployment phase, the inference time is also significantly reduced to 0.1 ms per sample. We also demonstrate the generalisation capability of the proposed method by evaluating models using different target movement characteristics to the ones in which they were trained.

Recognizing Bengali Word Images - A Zero-Shot Learning Perspective

Sukalpa Chanda, Daniël Arjen Willem Haitink, Prashant Kumar Prasad, Jochem Baas, Umapada Pal, Lambert Schomaker
Track 4: Document and Media Analysis
Tue 12 Jan 2021 at 17:00 in session PS T4.1

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Auto-TLDR; Zero-Shot Learning for Word Recognition in Bengali Script

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Zero-Shot Learning(ZSL) techniques could classify a completely unseen class, which it has never seen before during training. Thus, making it more apt for any real-life classification problem, where it is not possible to train a system with annotated data for all possible class types. This work investigates recognition of word images written in Bengali Script in a ZSL framework. The proposed approach performs Zero-Shot word recognition by coupling deep learned features procured from VGG16 architecture along with 13 basic shapes/stroke primitives commonly observed in Bengali script characters. As per the notion of ZSL framework those 13 basic shapes are termed as “Signature Attributes”. The obtained results are promising while evaluation was carried out in a Five-Fold cross-validation setup dealing with samples from 250 word classes.

S2I-Bird: Sound-To-Image Generation of Bird Species Using Generative Adversarial Networks

Joo Yong Shim, Joongheon Kim, Jong-Kook Kim
Track 3: Computer Vision Robotics and Intelligent Systems
Thu 14 Jan 2021 at 14:00 in session PS T3.8

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Auto-TLDR; Generating bird images from sound using conditional generative adversarial networks

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Generating images from sound is a challenging task. This paper proposes a novel deep learning model that generates bird images from their corresponding sound information. Our proposed model includes a sound encoder in order to extract suitable feature representations from audio recordings, and then it generates bird images that corresponds to its calls using conditional generative adversarial networks (GANs) with auxiliary classifiers. We demonstrate that our model produces better image generation results which outperforms other state-of-the-art methods in a similar context.

Unsupervised Co-Segmentation for Athlete Movements and Live Commentaries Using Crossmodal Temporal Proximity

Yasunori Ohishi, Yuki Tanaka, Kunio Kashino
Track 5: Image and Signal Processing
Fri 15 Jan 2021 at 15:00 in session PS T5.7

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Auto-TLDR; A guided attention scheme for audio-visual co-segmentation

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Audio-visual co-segmentation is a task to extract segments and regions corresponding to specific events on unlabelled audio and video signals. It is particularly important to accomplish it in an unsupervised way, since it is generally very difficult to manually label all the objects and events appearing in audio-visual signals for supervised learning. Here, we propose to take advantage of temporal proximity of corresponding audio and video entities included in the signals. For this purpose, we newly employ a guided attention scheme to this task to efficiently detect and utilize temporal cooccurrences of audio and video information. The experiments using a real TV broadcasting of Sumo wrestling, a sport event, with live commentaries show that our model can automatically extract specific athlete movements and its spoken descriptions in an unsupervised manner.

Merged 1D-2D Deep Convolutional Neural Networks for Nerve Detection in Ultrasound Images

Mohammad Alkhatib, Adel Hafiane, Pierre Vieyres
Track 3: Computer Vision Robotics and Intelligent Systems
Wed 13 Jan 2021 at 16:30 in session PS T3.5

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Auto-TLDR; A Deep Neural Network for Deep Neural Networks to Detect Median Nerve in Ultrasound-Guided Regional Anesthesia

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Ultrasound-Guided Regional Anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management. It offers the advantages of the targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. However, nerve detection is one of the most challenging tasks that anesthetists can encounter in the UGRA procedure. A computer-aided system that can detect automatically the nerve region would facilitate the anesthetist's daily routine and allow them to concentrate more on the anesthetic delivery. In this paper, we propose a new method based on merging deep learning models from different data to detect the median nerve. The merged architecture consists of two branches, one being one dimensional (1D) convolutional neural networks (CNN) branch and another 2D CNN branch. The merged architecture aims to learn the high-level features from 1D handcrafted noise-robust features and 2D ultrasound images. The obtained results show the validity and high accuracy of the proposed approach and its robustness.

EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial Pattern

Debashis Das Chakladar, Shubhashis Dey, Partha Pratim Roy, Masakazu Iwamura
Track 2: Biometrics, Human Analysis and Behavior Understanding
Wed 13 Jan 2021 at 14:00 in session PS T2.3

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Auto-TLDR; A Deep Ensemble Model for Cognitive State Assessment using EEG-based Cognitive State Analysis

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Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not efficiently used for cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial Pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial Pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. The proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. The output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of individual model prediction. This proposed model achieves 87\% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.

Documents Counterfeit Detection through a Deep Learning Approach

Darwin Danilo Saire Pilco, Salvatore Tabbone
Track 4: Document and Media Analysis
Tue 12 Jan 2021 at 17:00 in session PS T4.1

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Auto-TLDR; End-to-End Learning for Counterfeit Documents Detection using Deep Neural Network

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The main topic of this work is on the detection of counterfeit documents and especially banknotes. We propose an end-to-end learning model using a deep learning approach based on Adapnet++ which manages feature extraction at multiple scale levels using several residual units. Unlike previous models based on regions of interest (ROI) and high-resolution documents, our network is feed with simple input images (i.e., a single patch) and we do not need high resolution images. Besides, discriminative regions can be visualized at different scales. Our network learns by itself which regions of interest predict the better results. Experimental results show that we are competitive compared with the state-of-the-art and our deep neural network has good ability to generalize and can be applied to other kind of documents like identity or administrative one.