Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
Towards Robust Few-shot Class Incremental Learning in Audio Classification using Contrastive Representation
In machine learning applications, gradual data ingress is common, especially in audio processing where incremental learning is vital for real-time analytics. Few-shot class-incremental learning addresses challenges arisi…
Audio Classificationclass-incremental learningClass Incremental LearningContrastive Learning+2Multi-label audio classification with a noisy zero-shot teacher
We propose a novel training scheme using self-label correction and data augmentation methods designed to deal with noisy labels and improve real-world accuracy on a polyphonic audio content detection task. The augmentati…
Audio ClassificationData AugmentationAdvancing Continual Learning for Robust Deepfake Audio Classification
The emergence of new spoofing attacks poses an increasing challenge to audio security. Current detection methods often falter when faced with unseen spoofing attacks. Traditional strategies, such as retraining with new d…
Audio ClassificationClassificationContinual LearningFace SwappingElasticAST: An Audio Spectrogram Transformer for All Length and Resolutions
Transformers have rapidly overtaken CNN-based architectures as the new standard in audio classification. Transformer-based models, such as the Audio Spectrogram Transformers (AST), also inherit the fixed-size input parad…
AllAudio ClassificationDASS: Distilled Audio State Space Models Are Stronger and More Duration-Scalable Learners
State-space models (SSMs) have emerged as an alternative to Transformers for audio modeling due to their high computational efficiency with long inputs. While recent efforts on Audio SSMs have reported encouraging result…
Audio ClassificationAudio TaggingComputational EfficiencyKnowledge Distillation+1Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions
This work is concerned with devising a robust Parkinson's (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods. To this end, we firs…
Audio ClassificationParkinson Detection from SpeechSpeech EnhancementFully Few-shot Class-incremental Audio Classification Using Expandable Dual-embedding Extractor
It's assumed that training data is sufficient in base session of few-shot class-incremental audio classification. However, it's difficult to collect abundant samples for model training in base session in some practical s…
Audio ClassificationFastAST: Accelerating Audio Spectrogram Transformer via Token Merging and Cross-Model Knowledge Distillation
Audio classification models, particularly the Audio Spectrogram Transformer (AST), play a crucial role in efficient audio analysis. However, optimizing their efficiency without compromising accuracy remains a challenge. …
Audio ClassificationKnowledge DistillationBTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification
Respiratory sound classification (RSC) is challenging due to varied acoustic signatures, primarily influenced by patient demographics and recording environments. To address this issue, we introduce a text-audio multimoda…
Audio ClassificationSound ClassificationContrastive Learning from Synthetic Audio Doppelgängers
Learning robust audio representations currently demands extensive datasets of real-world sound recordings. By applying artificial transformations to these recordings, models can learn to recognize similarities despite su…
Audio ClassificationContrastive LearningDiversityAudio Mamba: Bidirectional State Space Model for Audio Representation Learning
Transformers have rapidly become the preferred choice for audio classification, surpassing methods based on CNNs. However, Audio Spectrogram Transformers (ASTs) exhibit quadratic scaling due to self-attention. The remova…
Audio ClassificationClassificationMambaRepresentation Learning+1M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation
Contrastive language-audio pre-training (CLAP) enables zero-shot (ZS) inference of audio and exhibits promising performance in several classification tasks. However, conventional audio representations are still crucial f…
Audio ClassificationLinear evaluationSelf-Supervised LearningTransfer Learninganimal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics
Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are beco…
Audio ClassificationBenchmarkingFew-Shot LearningSSAMBA: Self-Supervised Audio Representation Learning with Mamba State Space Model
Transformers have revolutionized deep learning across various tasks, including audio representation learning, due to their powerful modeling capabilities. However, they often suffer from quadratic complexity in both GPU …
Audio ClassificationGPUKeyword SpottingMamba+3Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification
In light of the COVID-19 pandemic, the early diagnosis of respiratory diseases has become increasingly crucial. Traditional diagnostic methods such as computed tomography (CT) and magnetic resonance imaging (MRI), whil…
Audio ClassificationComputed Tomography (CT)Diagnosticimage-classification+2Investigating Design Choices in Joint-Embedding Predictive Architectures for General Audio Representation Learning
This paper addresses the problem of self-supervised general-purpose audio representation learning. We explore the use of Joint-Embedding Predictive Architectures (JEPA) for this task, which consists of splitting an input…
Audio ClassificationRepresentation LearningOn-device Online Learning and Semantic Management of TinyML Systems
Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation pre…
Audio Classificationimage-classificationImage ClassificationManagement+1Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory Diseases
Compared with invasive examinations that require tissue sampling, respiratory sound testing is a non-invasive examination method that is safer and easier for patients to accept. In this study, we introduce Rene, a pionee…
Audio ClassificationDiagnosticDisease PredictionEvent Detection+2AFEN: Respiratory Disease Classification using Ensemble Learning
We present AFEN (Audio Feature Ensemble Learning), a model that leverages Convolutional Neural Networks (CNN) and XGBoost in an ensemble learning fashion to perform state-of-the-art audio classification for a range of re…
Audio ClassificationClassificationData AugmentationDiversity+1Benchmarking Representations for Speech, Music, and Acoustic Events
Limited diversity in standardized benchmarks for evaluating audio representation learning (ARL) methods may hinder systematic comparison of current methods' capabilities. We present ARCH, a comprehensive benchmark for ev…
Audio ClassificationBenchmarkingDiversityRepresentation Learning