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Audio Classification

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하위 태스크: 4
평가표: 26
보존된 평가 기록: 199

Audio Classification is a machine learning task that involves identifying and tagging audio signals into different classes or categories. The goal of audio classification is to enable machines to automatically recognize and distinguish between different types of audio, such as music, speech, and environmental sounds.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/kaen2891/bts

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BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory Sound Classification

https://github.com/kaen2891/stethoscope-guided_supervised_contrastive_learning

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Stethoscope-guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification

https://github.com/kamalesh0406/Audio-Classification

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Rethinking CNN Models for Audio Classification

https://github.com/kkoutini/cpjku_dcase20

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Receptive Field Regularization Techniques for Audio Classification and Tagging with Deep Convolutional Neural Networks

https://github.com/kkoutini/passt

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Efficient Training of Audio Transformers with Patchout

https://github.com/knoriy/CLARA

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CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition

https://github.com/kuai-lab/sound-guided-semantic-image-manipulation

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Sound-Guided Semantic Image Manipulation

https://github.com/kumar-shubham-ml/kaggle-birdclef-2021

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Weakly-Supervised Classification and Detection of Bird Sounds in the Wild.

https://github.com/leicheng-no/cdil-cnn

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Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks

https://github.com/limunimi/feature-learning-medical-acoustics

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Deep Feature Learning for Medical Acoustics

https://github.com/linzhiqiu/cross_modal_adaptation

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Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

https://github.com/liuxubo717/simpfs

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Simple Pooling Front-ends For Efficient Audio Classification

https://github.com/livingingroups/animal2vec

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animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics

https://github.com/lizeyujack/oceanship

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Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition

https://github.com/lukashedegaard/continual-transformers

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Continual Transformers: Redundancy-Free Attention for Online Inference

https://github.com/mapolinario94/s-tllr

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S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks

https://github.com/mhamzaerol/audio-mamba-aum

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Audio Mamba: Bidirectional State Space Model for Audio Representation Learning

https://github.com/microsoft/RespireNet

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RespireNet: A Deep Neural Network for Accurately Detecting Abnormal Lung Sounds in Limited Data Setting

https://github.com/microsoft/unilm/tree/master/beats

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BEATs: Audio Pre-Training with Acoustic Tokenizers

https://github.com/mohaimenz/acdnet

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Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

https://github.com/mohaimenz/pruning-xnor

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Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devices

https://github.com/multitel-ai/urban-sound-classification-and-comparison

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Urban Sound Classification : striving towards a fair comparison

https://github.com/multitel-ai/urban-sound-tagging

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CRNNs for Urban Sound Tagging with spatiotemporal context

https://github.com/mvrl/taxabind

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TaxaBind: A Unified Embedding Space for Ecological Applications

https://github.com/nttcslab/eval-audio-repr

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Masked Modeling Duo: Towards a Universal Audio Pre-training Framework

https://github.com/nttcslab/m2d

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Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input

https://github.com/opengvlab/internvideo

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InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

https://github.com/opengvlab/internvideo2

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InternVideo2: Scaling Foundation Models for Multimodal Video Understanding

https://github.com/patrick-kidger/generalised_shapelets

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Generalised Interpretable Shapelets for Irregular Time Series

https://github.com/pku-yuangroup/languagebind

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LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

https://github.com/pritamqu/CrissCross

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Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity

https://github.com/qwenlm/qwen-audio

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Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models

https://github.com/raymin0223/patch-mix_contrastive_learning

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Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification

https://github.com/retrocirce/hts-audio-transformer

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HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection

https://github.com/ristea/septr

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SepTr: Separable Transformer for Audio Spectrogram Processing

https://github.com/sainathadapa/urban-sound-tagging

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Urban Sound Tagging using Convolutional Neural Networks

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