DenseRecognition of Spoken Languages

Jaybrata Chakraborty, Bappaditya Chakraborty, Ujjwal Bhattacharya

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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.

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

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

Antonio Greco, Antonio Roberto, Alessia Saggese, Mario Vento

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

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

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

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

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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.

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

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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.

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

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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.

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Kin Wai Cheuk, Yin-Jyun Luo, Emmanouil Benetos, Herremans Dorien

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

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Digit Recognition Applied to Reconstructed Audio Signals Using Deep Learning

Anastasia-Sotiria Toufa, Constantine Kotropoulos

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

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

Michelangelo Acconcjaioco, Stavros Ntalampiras

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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

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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.

Learning Visual Voice Activity Detection with an Automatically Annotated Dataset

Stéphane Lathuiliere, Pablo Mesejo, Radu Horaud

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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.

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

Hong Liu, Yawei Wang, Bing Yang

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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.

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

Konstantinos Pyrovolakis, Paraskevi Tzouveli, Giorgos Stamou

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

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

Thi Phuong Thao Ha, Bt Balamurali, Herremans Dorien, Roig Gemma

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

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Adversarially Training for Audio Classifiers

Raymel Alfonso Sallo, Mohammad Esmaeilpour, Patrick Cardinal

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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.

Spatial Bias in Vision-Based Voice Activity Detection

Kalin Stefanov, Mohammad Adiban, Giampiero Salvi

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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.

Improving Gravitational Wave Detection with 2D Convolutional Neural Networks

Siyu Fan, Yisen Wang, Yuan Luo, Alexander Michael Schmitt, Shenghua Yu

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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

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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.

Influence of Event Duration on Automatic Wheeze Classification

Bruno M Rocha, Diogo Pessoa, Alda Marques, Paulo Carvalho, Rui Pedro Paiva

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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.

Anticipating Activity from Multimodal Signals

Tiziana Rotondo, Giovanni Maria Farinella, Davide Giacalone, Sebastiano Mauro Strano, Valeria Tomaselli, Sebastiano Battiato

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Auto-TLDR; Exploiting Multimodal Signal Embedding Space for Multi-Action Prediction

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Images, videos, audio signals, sensor data, can be easily collected in huge quantity by different devices and processed in order to emulate the human capability of elaborating a variety of different stimuli. Are multimodal signals useful to understand and anticipate human actions if acquired from the user viewpoint? This paper proposes to build an embedding space where inputs of different nature, but semantically correlated, are projected in a new representation space and properly exploited to anticipate the future user activity. To this purpose, we built a new multimodal dataset comprising video, audio, tri-axial acceleration, angular velocity, tri-axial magnetic field, pressure and temperature. To benchmark the proposed multimodal anticipation challenge, we consider classic classifiers on top of deep learning methods used to build the embedding space representing multimodal signals. The achieved results show that the exploitation of different modalities is useful to improve the anticipation of the future activity.

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

Yasunori Ohishi, Yuki Tanaka, Kunio Kashino

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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.

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

Zhuo Chen, Fei Yin, Xu-Yao Zhang, Qing Yang, Cheng-Lin Liu

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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.

Trainable Spectrally Initializable Matrix Transformations in Convolutional Neural Networks

Michele Alberti, Angela Botros, Schuetz Narayan, Rolf Ingold, Marcus Liwicki, Mathias Seuret

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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.

RWF-2000: An Open Large Scale Video Database for Violence Detection

Ming Cheng, Kunjing Cai, Ming Li

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Auto-TLDR; Flow Gated Network for Violence Detection in Surveillance Cameras

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In recent years, surveillance cameras are widely deployed in public places, and the general crime rate has been reduced significantly due to these ubiquitous devices. Usually, these cameras provide cues and evidence after crimes were conducted, while they are rarely used to prevent or stop criminal activities in time. It is both time and labor consuming to manually monitor a large amount of video data from surveillance cameras. Therefore, automatically recognizing violent behaviors from video signals becomes essential. In this paper, we summarize several existing video datasets for violence detection and propose a new video dataset with 2,000 videos all captured by surveillance cameras in real-world scenes. Also, we present a new method that utilizes both the merits of 3D-CNNs and optical flow, namely Flow Gated Network. The proposed approach obtains an accuracy of 87.25% on the test set of our proposed RWF-2000 database. The proposed database and source codes of this paper are currently open to access.

On the Information of Feature Maps and Pruning of Deep Neural Networks

Mohammadreza Soltani, Suya Wu, Jie Ding, Robert Ravier, Vahid Tarokh

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Auto-TLDR; Compressing Deep Neural Models Using Mutual Information

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A technique for compressing deep neural models achieving competitive performance to state-of-the-art methods is proposed. The approach utilizes the mutual information between the feature maps and the output of the model in order to prune the redundant layers of the network. Extensive numerical experiments on both CIFAR-10, CIFAR-100, and Tiny ImageNet data sets demonstrate that the proposed method can be effective in compressing deep models, both in terms of the numbers of parameters and operations. For instance, by applying the proposed approach to DenseNet model with 0.77 million parameters and 293 million operations for classification of CIFAR-10 data set, a reduction of 62.66% and 41.00% in the number of parameters and the number of operations are respectively achieved, while increasing the test error only by less than 1%.

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

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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.

Graph Convolutional Neural Networks for Power Line Outage Identification

Jia He, Maggie Cheng

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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.

Visual Oriented Encoder: Integrating Multimodal and Multi-Scale Contexts for Video Captioning

Bang Yang, Yuexian Zou

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Auto-TLDR; Visual Oriented Encoder for Video Captioning

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Video captioning is a challenging task which aims at automatically generating a natural language description of a given video. Recent researches have shown that exploiting the intrinsic multi-modalities of videos significantly promotes captioning performance. However, how to integrate multi-modalities to generate effective semantic representations for video captioning is still an open issue. Some researchers proposed to learn multimodal features in parallel during the encoding stage. The downside of these methods lies in the neglect of the interaction among multi-modalities and their rich contextual information. In this study, inspired by the fact that visual contents are generally more important for comprehending videos, we propose a novel Visual Oriented Encoder (VOE) to integrate multimodal features in an interactive manner. Specifically, VOE is designed as a hierarchical structure, where bottom layers are utilized to extract multi-scale contexts from auxiliary modalities while the top layer is exploited to generate joint representations by considering both visual and contextual information. Following the encoder-decoder framework, we systematically develop a VOE-LSTM model and evaluate it on two mainstream benchmarks: MSVD and MSR-VTT. Experimental results show that the proposed VOE surpasses conventional encoders and our VOE-LSTM model achieves competitive results compared with state-of-the-art approaches.

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

Jianrong Wang, Tong Wu, Shanyu Wang, Mei Yu, Qiang Fang, Ju Zhang, Li Liu

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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.

Person Recognition with HGR Maximal Correlation on Multimodal Data

Yihua Liang, Fei Ma, Yang Li, Shao-Lun Huang

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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.

Detecting Marine Species in Echograms Via Traditional, Hybrid, and Deep Learning Frameworks

Porto Marques Tunai, Alireza Rezvanifar, Melissa Cote, Alexandra Branzan Albu, Kaan Ersahin, Todd Mudge, Stephane Gauthier

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Auto-TLDR; End-to-End Deep Learning for Echogram Interpretation of Marine Species in Echograms

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This paper provides a comprehensive comparative study of traditional, hybrid, and deep learning (DL) methods for detecting marine species in echograms. Acoustic backscatter data obtained from multi-frequency echosounders is visualized as echograms and typically interpreted by marine biologists via manual or semi-automatic methods, which are time-consuming. Challenges related to automatic echogram interpretation are the variable size and acoustic properties of the biological targets (marine life), along with significant inter-class similarities. Our study explores and compares three types of approaches that cover the entire range of machine learning methods. Based on our experimental results, we conclude that an end-to-end DL-based framework, that can be readily scaled to accommodate new species, is overall preferable to other learning approaches for echogram interpretation, even when only a limited number of annotated training samples is available.

Single-Modal Incremental Terrain Clustering from Self-Supervised Audio-Visual Feature Learning

Reina Ishikawa, Ryo Hachiuma, Akiyoshi Kurobe, Hideo Saito

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Auto-TLDR; Multi-modal Variational Autoencoder for Terrain Type Clustering

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The key to an accurate understanding of terrain is to extract the informative features from the multi-modal data obtained from different devices. Sensors, such as RGB cameras, depth sensors, vibration sensors, and microphones, are used as the multi-modal data. Many studies have explored ways to use them, especially in the robotics field. Some papers have successfully introduced single-modal or multi-modal methods. However, in practice, robots can be faced with extreme conditions; microphones do not work well in the crowded scenes, and an RGB camera cannot capture terrains well in the dark. In this paper, we present a novel framework using the multi-modal variational autoencoder and the Gaussian mixture model clustering algorithm on image data and audio data for terrain type clustering. Our method enables the terrain type clustering even if one of the modalities (either image or audio) is missing at the test-time. We evaluated the clustering accuracy with a conventional multi-modal terrain type clustering method and we conducted ablation studies to show the effectiveness of our approach.

Robust Audio-Visual Speech Recognition Based on Hybrid Fusion

Hong Liu, Wenhao Li, Bing Yang

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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.

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

Joo Yong Shim, Joongheon Kim, Jong-Kook Kim

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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.

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

Yongqiang Dou, Haocheng Yang, Maolin Yang, Yanyan Xu, Dengfeng Ke

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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.

Automatic Annotation of Corpora for Emotion Recognition through Facial Expressions Analysis

Alex Mircoli, Claudia Diamantini, Domenico Potena, Emanuele Storti

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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.

Are Multiple Cross-Correlation Identities Better Than Just Two? Improving the Estimate of Time Differences-Of-Arrivals from Blind Audio Signals

Danilo Greco, Jacopo Cavazza, Alessio Del Bue

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Auto-TLDR; Improving Blind Channel Identification Using Cross-Correlation Identity for Time Differences-of-Arrivals Estimation

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Given an unknown audio source, the estimation of time differences-of-arrivals (TDOAs) can be efficiently and robustly solved using blind channel identification and exploiting the cross-correlation identity (CCI). Prior "blind" works have improved the estimate of TDOAs by means of different algorithmic solutions and optimization strategies, while always sticking to the case N = 2 microphones. But what if we can obtain a direct improvement in performance by just increasing N? In this paper we try to investigate this direction, showing that, despite the arguable simplicity, this is capable of (sharply) improving upon state-of-the-art blind channel identification methods based on CCI, without modifying the computational pipeline. Inspired by our results, we seek to warm up the community and the practitioners by paving the way (with two concrete, yet preliminary, examples) towards joint approaches in which advances in the optimization are combined with an increased number of microphones, in order to achieve further improvements.

Wireless Localisation in WiFi Using Novel Deep Architectures

Peizheng Li, Han Cui, Aftab Khan, Usman Raza, Robert Piechocki, Angela Doufexi, Tim Farnham

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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.

Continuous Sign Language Recognition with Iterative Spatiotemporal Fine-Tuning

Kenessary Koishybay, Medet Mukushev, Anara Sandygulova

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Auto-TLDR; A Deep Neural Network for Continuous Sign Language Recognition with Iterative Gloss Recognition

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This paper aims to develop a deep neural network for Continuous Sign Language Recognition (CSLR) with iterative Gloss Recognition (GR) fine-tuning. CSLR has been a popular research field in the last years and iterative optimization methods are well established. This paper introduces our proposed architecture involving Spatiotemporal feature-extraction model to segment useful ``gloss-unit" features and BiLSTM with CTC as a sequence model. Spatiotemporal Feature Extractor is used for both image features extraction and sequence length reduction. To this end, we compare different architectures for feature extraction and sequence model. In addition, we iteratively fine-tune feature extractor on gloss-unit video segments with alignments from the end2end model. During the iterative training, we use novel alignment correction technique, which is based on minimum transformations of Levenshtein distance. All the experiments were conducted on the RWTH-PHOENIX-Weather-2014 dataset.

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

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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%.

Personalized Models in Human Activity Recognition Using Deep Learning

Hamza Amrani, Daniela Micucci, Paolo Napoletano

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Auto-TLDR; Incremental Learning for Personalized Human Activity Recognition

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Current sensor-based human activity recognition techniques that rely on a user-independent model struggle to generalize to new users and on to changes that a person may make over time to his or her way of carrying out activities. Incremental learning is a technique that allows to obtain personalized models which may improve the performance on the classifiers thanks to a continuous learning based on user data. Finally, deep learning techniques have been proven to be more effective with respect to traditional ones in the generation of user-independent models. The aim of our work is therefore to put together deep learning techniques with incremental learning in order to obtain personalized models that perform better with respect to user-independent model and personalized model obtained using traditional machine learning techniques. The experimentation was done by comparing the results obtained by a technique in the state of the art with those obtained by two neural networks (ResNet and a simplified CNN) on three datasets. The experimentation showed that neural networks adapt faster to a new user than the baseline.

Recursive Recognition of Offline Handwritten Mathematical Expressions

Marco Cotogni, Claudio Cusano, Antonino Nocera

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Auto-TLDR; Online Handwritten Mathematical Expression Recognition with Recurrent Neural Network

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In this paper we propose a method for Offline Handwritten Mathematical Expression recognition. The method is a fast and accurate thanks to its architecture, which include both a Convolutional Neural Network and a Recurrent Neural Network. The CNN extracts features from the image to recognize and its output is provided to the RNN which produces the mathematical expression encoded in the LaTeX language. To process both sequential and non-sequential mathematical expressions we also included a deconvolutional module which, in a recursive way, segments the image for additional analysis trough a recursive process. The results obtained show a very high accuracy obtained on a large handwritten data set of 9100 samples of handwritten expressions.

Modulation Pattern Detection Using Complex Convolutions in Deep Learning

Jakob Krzyston, Rajib Bhattacharjea, Andrew Stark

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Auto-TLDR; Complex Convolutional Neural Networks for Modulation Pattern Classification

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Telecommunications relies on transmitting and receiving signals containing specific modulation patterns in both the real and complex domains. Classifying modulation patterns is difficult because noise and poor signal to noise ratio (SNR) obfuscate the `input' signal. Although deep learning approaches have shown great promise over statistical methods in this problem space, deep learning frameworks have been developed to deal with exclusively real-valued data and are unable to compute convolutions for complex-valued data. In previous work, we have shown that CNNs using complex convolutions are able to classify modulation patterns by up to 35\% more accurately than comparable CNN architectures. In this paper, we demonstrate that enabling complex convolutions in CNNs are (1) up to 50\% better at recognizing modulation patterns in complex signals with high SNR when trained on low SNR data, and (2) up to 12\% better at recognizing modulation patterns in complex signals with low SNR when trained on high SNR data. Additionally, we compare the features learned in each experiment by visualizing the inputs that results in one-hot modulation pattern classification for each network.