Papers Sound Classification
“Sound Classification” 태그가 달린 논문 148편 · 필터 해제
USAD: Universal Speech and Audio Representation via Distillation
Self-supervised learning (SSL) has revolutionized audio representations, yet models often remain domain-specific, focusing on either speech or non-speech tasks. In this work, we present Universal Speech and Audio Distill…
Audio TaggingRepresentation LearningSelf-Supervised LearningSound ClassificationAcoustic scattering AI for non-invasive object classifications: A case study on hair assessment
This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scat…
ClassificationDeep LearningPrivacy PreservingSound ClassificationDisentangling Dual-Encoder Masked Autoencoder for Respiratory Sound Classification
Deep neural networks have been applied to audio spectrograms for respiratory sound classification, but it remains challenging to achieve satisfactory performance due to the scarcity of available data. Moreover, domain mi…
DisentanglementSound ClassificationMUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification
Unsupervised Domain Adaptation (UDA) is essential for adapting machine learning models to new, unlabeled environments where data distribution shifts can degrade performance. Existing UDA algorithms are designed for singl…
ClassificationDomain AdaptationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Domain Adaptation Method and Modality Gap Impact in Audio-Text Models for Prototypical Sound Classification
Audio-text models are widely used in zero-shot environmental sound classification as they alleviate the need for annotated data. However, we show that their performance severely drops in the presence of background sound …
ClassificationDomain AdaptationEnvironmental Sound ClassificationSound ClassificationAdaptive Differential Denoising for Respiratory Sounds Classification
Automated respiratory sound classification faces practical challenges from background noise and insufficient denoising in existing systems. We propose Adaptive Differential Denoising network, that integrates noise suppre…
Audio ClassificationClassificationDenoisingDiagnostic+1General-purpose audio representation learning for real-world sound scenes
While audio foundation models perform well on myriad of tasks from sound classification to speech analysis, these models are trained and tested on dry, non-spatial, single-source audio clips. This limits their success in…
MambaRepresentation LearningSound ClassificationPatient Domain Supervised Contrastive Learning for Lung Sound Classification Using Mobile Phone
Auscultation is crucial for diagnosing lung diseases. The COVID-19 pandemic has revealed the limitations of traditional, in-person lung sound assessments. To overcome these issues, advancements in digital stethoscopes an…
Contrastive LearningDiagnosticLung Sound ClassificationSound ClassificationImproving Respiratory Sound Classification with Architecture-Agnostic Knowledge Distillation from Ensembles
Respiratory sound datasets are limited in size and quality, making high performance difficult to achieve. Ensemble models help but inevitably increase compute cost at inference time. Soft label training distills knowledg…
Knowledge DistillationSound ClassificationPatient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses
Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approac…
Audio ClassificationLung Sound ClassificationSound ClassificationThe Computation of Generalized Embeddings for Underwater Acoustic Target Recognition using Contrastive Learning
The increasing level of sound pollution in marine environments poses an increased threat to ocean health, making it crucial to monitor underwater noise. By monitoring this noise, the sources responsible for this pollutio…
Contrastive LearningSound ClassificationTri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis by combining the patient's medical histo…
DiagnosticLung Sound ClassificationSound ClassificationCan Masked Autoencoders Also Listen to Birds?
Masked Autoencoders (MAEs) have shown competitive results in audio classification by learning rich semantic representations through an efficient self-supervised reconstruction task. However, general-purpose models fail t…
Audio ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSelf-Supervised Learning+1Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning
Asthma is a chronic respiratory condition that affects millions of people worldwide. While this condition can be managed by administering controller medications through handheld inhalers, clinical studies have shown low …
ClassificationSelf-Supervised LearningSound ClassificationMind the Prompt: Prompting Strategies in Audio Generations for Improving Sound Classification
This paper investigates the design of effective prompt strategies for generating realistic datasets using Text-To-Audio (TTA) models. We also analyze different techniques for efficiently combining these datasets to enhan…
ClassificationData AugmentationSound ClassificationSymbolic Audio Classification via Modal Decision Tree Learning
The range of potential applications of acoustic analysis is wide. Classification of sounds, in particular, is a typical machine learning task that received a lot of attention in recent years. The most common approaches t…
Audio ClassificationClassificationEmotion ClassificationSound ClassificationWeakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
Convolutional Dictionary Learning (CDL) has emerged as a powerful approach for signal representation by learning translation-invariant features through convolution operations. While existing CDL methods are predominantly…
ClassificationDictionary LearningEnvironmental Sound ClassificationMulti-Label Classification+2CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets …
Audio ClassificationClusteringConstrained ClusteringContrastive Learning+2Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification
Respiratory sound classification plays a pivotal role in diagnosing respiratory diseases. While deep learning models have shown success with various respiratory sound datasets, our experiments indicate that models traine…
Data AugmentationSound ClassificationSound Classification of Four Insect Classes
The goal of this project is to classify four different insect sounds: cicada, beetle, termite, and cricket. One application of this project is for pest control to monitor and protect our ecosystem. Our project leverages …
ClassificationData AugmentationSound Classification