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Found 69 papers, 19 papers with code
Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction
This work presents a multitask approach to the simultaneous estimation of age, country of origin, and emotion given vocal burst audio for the 2022 ICML Expressive Vocalizations Challenge ExVo-MultiTask track.
Towards End-to-End Private Automatic Speaker Recognition
This poses two important issues: first, knowledge of the speaker embedding extraction model may create security and robustness liabilities for the authentication system, as this knowledge might help attackers in crafting adversarial examples able to mislead the system; second, from the point of view of a service provider the speaker embedding extraction model is arguably one of the most valuable components in the system and, as such, disclosing it would be highly undesirable.
Bear the Query in Mind: Visual Grounding with Query-conditioned Convolution
With our proposed QCM, the downstream fusion module receives visual features that are more discriminative and focused on the desired object described in the expression, leading to more accurate predictions.
FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
Based on the analysis, we hence propose FreeMatch to define and adjust the confidence threshold in a self-adaptive manner according to the model's learning status.
On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice
Lately, there has been a global effort by multiple research groups to detect COVID-19 from voice.
Recent improvements of ASR models in the face of adversarial attacks
Like many other tasks involving neural networks, Speech Recognition models are vulnerable to adversarial attacks.
Point3D: tracking actions as moving points with 3D CNNs
Our Point3D consists of a Point Head for action localization and a 3D Head for action classification.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
Ontological Learning from Weak Labels
We find that the baseline Siamese does not perform better by incorporating ontology information in the weak and multi-label scenario, but that the GCN does capture the ontology knowledge better for weak, multi-labeled data.
Sequential Randomized Smoothing for Adversarially Robust Speech Recognition
We apply adaptive versions of state-of-the-art attacks, such as the Imperceptible ASR attack, to our model, and show that our strongest defense is robust to all attacks that use inaudible noise, and can only be broken with very high distortion.
Self-Supervised 3D Face Reconstruction via Conditional Estimation
We present a conditional estimation (CEST) framework to learn 3D facial parameters from 2D single-view images by self-supervised training from videos.
SphereFace Revived: Unifying Hyperspherical Face Recognition
As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin.
The Right to Talk: An Audio-Visual Transformer Approach
Therefore, this work introduces a new Audio-Visual Transformer approach to the problem of localization and highlighting the main speaker in both audio and visual channels of a multi-speaker conversation video in the wild.
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization.
Controlled AutoEncoders to Generate Faces from Voices
With this in perspective, we propose a framework to morph a target face in response to a given voice in a way that facial features are implicitly guided by learned voice-face correlation in this paper.
Improving weakly supervised sound event detection with self-supervised auxiliary tasks
To that extent, we propose a shared encoder architecture with sound event detection as a primary task and an additional secondary decoder for a self-supervised auxiliary task.
Training image classifiers using Semi-Weak Label Data
Thus, this paper introduces a novel semi-weak label learning paradigm as a middle ground to mitigate the problem.
Constant Random Perturbations Provide Adversarial Robustness with Minimal Effect on Accuracy
This paper proposes an attack-independent (non-adversarial training) technique for improving adversarial robustness of neural network models, with minimal loss of standard accuracy.
Contrast and Order Representations for Video Self-Supervised Learning
To address this, we present a contrast-and-order representation (CORP) framework for learning self-supervised video representations that can automatically capture both the appearance information within each frame and temporal information across different frames.
Is normalization indispensable for training deep neural network?
In this paper, we study what would happen when normalization layers are removed from the network, and show how to train deep neural networks without normalization layers and without performance degradation.
FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances
Speaker identification models are vulnerable to carefully designed adversarial perturbations of their input signals that induce misclassification.
Masked Proxy Loss For Text-Independent Speaker Verification
We further propose Multinomial Masked Proxy (MMP) loss to leverage the hardness of speaker pairs.
Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection
Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem.
Exploiting Non-Linear Redundancy for Neural Model Compression
Deploying deep learning models, comprising of non-linear combination of millions, even billions, of parameters is challenging given the memory, power and compute constraints of the real world.
Automatic In-the-wild Dataset Annotation with Deep Generalized Multiple Instance Learning
The automation of the diagnosis and monitoring of speech affecting diseases in real life situations, such as Depression or Parkinson{'}s disease, depends on the existence of rich and large datasets that resemble real life conditions, such as those collected from in-the-wild multimedia repositories like YouTube.
Face Reconstruction from Voice using Generative Adversarial Networks
The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set.
The phonetic bases of vocal expressed emotion: natural versus acted
Our tests show significant differences in the manner and choice of phonemes in acted and natural speech, concluding moderate to low validity and value in using acted speech databases for emotion classification tasks.
Detecting gender differences in perception of emotion in crowdsourced data
While we limit ourselves to a single modality (i. e. speech), our framework is applicable to studies of emotion perception from all such loosely annotated data in general.
Non-Determinism in Neural Networks for Adversarial Robustness
Recent breakthroughs in the field of deep learning have led to advancements in a broad spectrum of tasks in computer vision, audio processing, natural language processing and other areas.
Reconstructing faces from voices
Voice profiling aims at inferring various human parameters from their speech, e. g. gender, age, etc.
Nonlinear Semi-Parametric Models for Survival Analysis
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis.
Hierarchical Routing Mixture of Experts
Addressing these problems, we propose a binary tree-structured hierarchical routing mixture of experts (HRME) model that has classifiers as non-leaf node experts and simple regression models as leaf node experts.
Hide and Speak: Towards Deep Neural Networks for Speech Steganography
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier.
Learning Sound Events From Webly Labeled Data
In the last couple of years, weakly labeled learning for sound events has turned out to be an exciting approach for audio event detection.
Higher-order Network for Action Recognition
Capturing spatiotemporal dynamics is an essential topic in video recognition.
Neural Regression Trees
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one.
Neural Regression Tree
Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one.
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces.
Optimal Strategies for Matching and Retrieval Problems by Comparing Covariates
In many retrieval problems, where we must retrieve one or more entries from a gallery in response to a probe, it is common practice to learn to do by directly comparing the probe and gallery entries to one another.
A Closer Look at Weak Label Learning for Audio Events
In this work, we first describe a CNN based approach for weakly supervised training of audio events.
Voice Impersonation using Generative Adversarial Networks
In voice impersonation, the resultant voice must convincingly convey the impression of having been naturally produced by the target speaker, mimicking not only the pitch and other perceivable signal qualities, but also the style of the target speaker.
Sound Audio and Speech Processing
Framework for evaluation of sound event detection in web videos
The framework crawls videos using search queries corresponding to 78 sound event labels drawn from three datasets.
Be Careful What You Backpropagate: A Case For Linear Output Activations & Gradient Boosting
To this end, we demonstrate faster convergence and better performance on diverse classification tasks: image classification using CIFAR-10 and ImageNet, and semantic segmentation using PASCAL VOC 2012.
Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data
We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data.
SphereFace: Deep Hypersphere Embedding for Face Recognition
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.
Ranked #1 on
Face Verification
on CK+
On the Origin of Deep Learning
This paper is a review of the evolutionary history of deep learning models.
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning
How much can pruning algorithms teach us about the fundamentals of learning representations in neural networks?
Audio Event and Scene Recognition: A Unified Approach using Strongly and Weakly Labeled Data
In this paper we propose a novel learning framework called Supervised and Weakly Supervised Learning where the goal is to learn simultaneously from weakly and strongly labeled data.
Discovering Sound Concepts and Acoustic Relations In Text
In this paper we describe approaches for discovering acoustic concepts and relations in text.
An Approach for Self-Training Audio Event Detectors Using Web Data
The audio event detectors are trained on the labeled audio and ran on the unlabeled audio downloaded from YouTube.
Features and Kernels for Audio Event Recognition
One of the most important problems in audio event detection research is absence of benchmark results for comparison with any proposed method.
Sound Multimedia
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
One contribution is the previously unavailable documentation of the challenges and implications of collecting audio recordings with these type of labels.
Classifier Risk Estimation under Limited Labeling Resources
In this paper we propose strategies for estimating performance of a classifier when labels cannot be obtained for the whole test set.
Weakly Supervised Scalable Audio Content Analysis
Audio Event Detection is an important task for content analysis of multimedia data.
Audio Event Detection using Weakly Labeled Data
This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.
Content-based Video Indexing and Retrieval Using Corr-LDA
Existing video indexing and retrieval methods on popular web-based multimedia sharing websites are based on user-provided sparse tagging.
Environmental Noise Embeddings for Robust Speech Recognition
We propose a novel deep neural network architecture for speech recognition that explicitly employs knowledge of the background environmental noise within a deep neural network acoustic model.
Handcrafted Local Features are Convolutional Neural Networks
We approach this problem by first showing that local handcrafted features and Convolutional Neural Networks (CNNs) share the same convolution-pooling network structure.
A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas
Further, we will also look into the development history of modelling time series data with neural networks.
Privacy-Preserving Multi-Document Summarization
State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties.
Plagiarism Detection in Polyphonic Music using Monaural Signal Separation
Given the large number of new musical tracks released each year, automated approaches to plagiarism detection are essential to help us track potential violations of copyright.
Unsupervised Fusion Weight Learning in Multiple Classifier Systems
We also introduce a novel metric for ranking instances based on an index which depends upon the rank of weighted scores of test points among the weighted scores of training points.
Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition
MIFS compensates for information lost from using differential operators by recapturing information at coarse scales.
Unsupervised Structure Discovery for Semantic Analysis of Audio
Approaches to audio classification and retrieval tasks largely rely on detection-based discriminative models.
Learning Model-Based Sparsity via Projected Gradient Descent
As an example we elaborate on application of the main results to estimation in Generalized Linear Model.
Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers
As increasing amounts of sensitive personal information finds its way into data repositories, it is important to develop analysis mechanisms that can derive aggregate information from these repositories without revealing information about individual data instances.
A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds
In this paper we present an algorithm for separating mixed sounds from a monophonic recording.
Sparse Overcomplete Latent Variable Decomposition of Counts Data
An important problem in many fields is the analysis of counts data to extract meaningful latent components.
