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Found 30 papers, 7 papers with code
Recursive Joint Attention for Audio-Visual Fusion in Regression based Emotion Recognition
In video-based emotion recognition (ER), it is important to effectively leverage the complementary relationship among audio (A) and visual (V) modalities, while retaining the intra-modal characteristics of individual modalities.
Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attention
In this paper, we focus on dimensional ER based on the fusion of facial and vocal modalities extracted from videos, where complementary audio-visual (A-V) relationships are explored to predict an individual's emotional states in valence-arousal space.
RSD-GAN: Regularized Sobolev Defense GAN Against Speech-to-Text Adversarial Attacks
This paper introduces a new synthesis-based defense algorithm for counteracting with a varieties of adversarial attacks developed for challenging the performance of the cutting-edge speech-to-text transcription systems.
RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis
We refer to this noble definition as compound conditional vector and employ it for training the generator network.
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks
This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network, namely ResNet-18.
A Joint Cross-Attention Model for Audio-Visual Fusion in Dimensional Emotion Recognition
Specifically, we propose a joint cross-attention model that relies on the complementary relationships to extract the salient features across A-V modalities, allowing for accurate prediction of continuous values of valence and arousal.
Bi-Discriminator Class-Conditional Tabular GAN
This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns.
Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognition
Results indicate that our cross-attentional A-V fusion model is a cost-effective approach that outperforms state-of-the-art fusion approaches.
Towards Robust Speech-to-Text Adversarial Attack
This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo.
Multi-Discriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems
This paper introduces a defense approach against end-to-end adversarial attacks developed for cutting-edge speech-to-text systems.
Weakly Supervised Learning for Facial Behavior Analysis : A Review
In this paper, we provide a comprehensive review of weakly supervised learning (WSL) approaches for facial behavior analysis with both categorical as well as dimensional labels along with the challenges and potential research directions associated with it.
Deep DA for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labeled Videos
The WSDA-OR model enforces ordinal relationships among the intensity levels as-signed to the target sequences, and associates multiple relevant frames to sequence-level labels (instead of a single frame).
Class-Conditional Defense GAN Against End-to-End Speech Attacks
In this paper we propose a novel defense approach against end-to-end adversarial attacks developed to fool advanced speech-to-text systems such as DeepSpeech and Lingvo.
Conditioning Trick for Training Stable GANs
In this paper we propose a conditioning trick, called difference departure from normality, applied on the generator network in response to instability issues during GAN training.
Adversarially Training for Audio Classifiers
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.
Deep Domain Adaptation for Ordinal Regression of Pain Intensity Estimation Using Weakly-Labelled Videos
Estimation of pain intensity from facial expressions captured in videos has an immense potential for health care applications.
Improving Stability of LS-GANs for Audio and Speech Signals
In this paper we address the instability issue of generative adversarial network (GAN) by proposing a new similarity metric in unitary space of Schur decomposition for 2D representations of audio and speech signals.
From Sound Representation to Model Robustness
In this paper, we investigate the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network.
Detection of Adversarial Attacks and Characterization of Adversarial Subspace
Adversarial attacks have always been a serious threat for any data-driven model.
Deep Weakly-Supervised Domain Adaptation for Pain Localization in Videos
Automatic pain assessment has an important potential diagnostic value for populations that are incapable of articulating their pain experiences.
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling
Video-based emotion recognition is a challenging task because it requires to distinguish the small deformations of the human face that represent emotions, while being invariant to stronger visual differences due to different identities.
Universal Adversarial Audio Perturbations
We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios.
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition
This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition.
Bag-of-Audio-Words based on Autoencoder Codebook for Continuous Emotion Prediction
The conventional BoW model is based on a dictionary (codebook) built from elementary representations which are selected randomly or by using a clustering algorithm on a training dataset.
Emotion Recognition Using Fusion of Audio and Video Features
In this paper we propose a fusion approach to continuous emotion recognition that combines visual and auditory modalities in their representation spaces to predict the arousal and valence levels.
Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods
Automatic measuring of speaker sincerity degree is a novel research problem in computational paralinguistics.
A Robust Approach for Securing Audio Classification Against Adversarial Attacks
In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of the most common machine learning model, namely deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform (STFT), discrete wavelet transform (DWT) and cross recurrent plot (CRP) against several state-of-the-art adversarial attacks.
End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network
In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal.
Ranked #6 on
Environmental Sound Classification
on UrbanSound8K
(Accuracy metric, using extra
training data)
Environmental Sound Classification
General Classification
+1
Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical $K$-Means++ algorithm and a new architecture for high-level data augmentation.
Automatic Dialect Detection in Arabic Broadcast Speech
We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%.
