Papers Audio Classification
“Audio Classification” 태그가 달린 논문 413편 · 필터 해제
TaxaBind: A Unified Embedding Space for Ecological Applications
We present TaxaBind, a unified embedding space for characterizing any species of interest. TaxaBind is a multimodal embedding space across six modalities: ground-level images of species, geographic location, satellite im…
Audio ClassificationCross-Modal RetrievalZero-Shot Image ClassificationAudio Classification of Low Feature Spectrograms Utilizing Convolutional Neural Networks
Modern day audio signal classification techniques lack the ability to classify low feature audio signals in the form of spectrographic temporal frequency data representations. Additionally, currently utilized techniques …
Audio ClassificationClassificationA Novel Score-CAM based Denoiser for Spectrographic Signature Extraction without Ground Truth
Sonar based audio classification techniques are a growing area of research in the field of underwater acoustics. Usually, underwater noise picked up by passive sonar transducers contains all types of signals that travel …
Audio ClassificationDenoisingGenerative Adversarial NetworkBeyond Speech and More: Investigating the Emergent Ability of Speech Foundation Models for Classifying Physiological Time-Series Signals
Despite being trained exclusively on speech data, speech foundation models (SFMs) like Whisper have shown impressive performance in non-speech tasks such as audio classification. This is partly because speech shares some…
Audio ClassificationTime SeriesSpectral and Rhythm Features for Audio Classification with Deep Convolutional Neural Networks
Convolutional neural networks (CNNs) are widely used in computer vision. They can be used not only for conventional digital image material to recognize patterns, but also for feature extraction from digital imagery repre…
Audio ClassificationRhythmA Multimodal Framework for Deepfake Detection
The rapid advancement of deepfake technology poses a significant threat to digital media integrity. Deepfakes, synthetic media created using AI, can convincingly alter videos and audio to misrepresent reality. This creat…
Audio ClassificationDeepFake DetectionFace SwappingMisinformationEpisodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation. Building upon such success, we first propose …
Audio ClassificationFew-Shot Audio ClassificationFew-Shot LearningSynthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data
We present Synthio, a novel approach for augmenting small-scale audio classification datasets with synthetic data. Our goal is to improve audio classification accuracy with limited labeled data. Traditional data augmenta…
Audio ClassificationCaption GenerationData AugmentationDiversityFrom Vision to Audio and Beyond: A Unified Model for Audio-Visual Representation and Generation
Video encompasses both visual and auditory data, creating a perceptually rich experience where these two modalities complement each other. As such, videos are a valuable type of media for the investigation of the interpl…
Audio ClassificationAudio GenerationRepresentation LearningDeFT-Mamba: Universal Multichannel Sound Separation and Polyphonic Audio Classification
This paper presents a framework for universal sound separation and polyphonic audio classification, addressing the challenges of separating and classifying individual sound sources in a multichannel mixture. The proposed…
Audio ClassificationClassificationMambaA sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followed by category names. In this work, we ex…
Audio ClassificationClassificationContrastive LearningPrompt Engineering+1ReCLAP: Improving Zero Shot Audio Classification by Describing Sounds
Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categories specified with natural language promp…
Audio ClassificationDescriptiveText RetrievalZero-shot Audio ClassificationA Framework for Synthetic Audio Conversations Generation using Large Language Models
In this paper, we introduce ConversaSynth, a framework designed to generate synthetic conversation audio using large language models (LLMs) with multiple persona settings. The framework first creates diverse and coherent…
Audio ClassificationAudio TaggingDiversityspeech-recognition+3Multi-label Zero-Shot Audio Classification with Temporal Attention
Zero-shot learning models are capable of classifying new classes by transferring knowledge from the seen classes using auxiliary information. While most of the existing zero-shot learning methods focused on single-label …
Audio ClassificationClassificationZero-shot Audio Classificationzero-shot-classification+1ModalityMirror: Improving Audio Classification in Modality Heterogeneity Federated Learning with Multimodal Distillation
Multimodal Federated Learning frequently encounters challenges of client modality heterogeneity, leading to undesired performances for secondary modality in multimodal learning. It is particularly prevalent in audiovisua…
Audio ClassificationFederated LearningKnowledge DistillationEAViT: External Attention Vision Transformer for Audio Classification
This paper presents the External Attention Vision Transformer (EAViT) model, a novel approach designed to enhance audio classification accuracy. As digital audio resources proliferate, the demand for precise and efficien…
Audio ClassificationClassificationRecommendation SystemsILDNet: A Novel Deep Learning Framework for Interstitial Lung Disease Identification Using Respiratory Sounds
Interstitial lung disease (ILD) is a collection of pulmonary adventitious conditions that induce scarring of the lung parenchyma, fibrosis, and inflammation. ILD encompasses over 200 chronic respiratory diseases that gra…
Audio ClassificationDiagnosticLung Disease ClassificationLung Sound Classification+3Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs
Research on multi-modal contrastive learning strategies for audio and text has rapidly gained interest. Contrastively trained Audio-Language Models (ALMs), such as CLAP, which establish a unified representation across au…
Audio ClassificationContrastive LearningRetrievalZero-shot Audio ClassificationDilated Convolution with Learnable Spacings
This thesis presents and evaluates the Dilated Convolution with Learnable Spacings (DCLS) method. Through various supervised learning experiments in the fields of computer vision, audio, and speech processing, the DCLS m…
Audio Classificationobject-detectionObject DetectionSemantic SegmentationContrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO2) esti…
Audio ClassificationBinary ClassificationregressionSpO2 estimation