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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/sonycslparis/audio-metrics

CNN Architectures for Large-Scale Audio Classification

https://github.com/sooheon/perceiver-jax

Perceiver: General Perception with Iterative Attention

https://github.com/stanfordmlgroup/aihc-sum20-lung-sounds

CNN Architectures for Large-Scale Audio Classification

https://github.com/stanfordmlgroup/selfsupervised-lungandheartsounds

CNN Architectures for Large-Scale Audio Classification

https://github.com/ta012/DTFAT

https://github.com/ta012/MaxAST

https://github.com/ta012/SSLAM

SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes

https://github.com/taki0112/AdamP-Tensorflow

AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights

https://github.com/towhee-io/towhee

CNN Architectures for Large-Scale Audio Classification

https://github.com/vincenzodentamaro/aucoresnet

https://github.com/yangyucheng000/University/tree/main/model-1/perceiver

Perceiver: General Perception with Iterative Attention

https://github.com/yliess86/Bayeformers

Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes

https://github.com/zhihaozhang97/ru-ai

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

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.