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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/facebookresearch/GDT

공식 코드

On Compositions of Transformations in Contrastive Self-Supervised Learning

https://github.com/facebookresearch/audiomae

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Masked Autoencoders that Listen

https://github.com/fadymedhat/MCLNN

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Masked Conditional Neural Networks for Audio Classification

https://github.com/fadymedhat/YorNoise

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Masked Conditional Neural Networks for Environmental Sound Classification

https://github.com/fmi-basel/stork

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Fluctuation-driven initialization for spiking neural network training

https://github.com/fschmid56/efficientat

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Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation

https://github.com/gbibbo/ai4s-embedded

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Audio Tagging on an Embedded Hardware Platform

https://github.com/gitmehrdad/face

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Face: Fast, Accurate and Context-Aware Audio Annotation and Classification

https://github.com/giuliotosato/autokeras-bioacustic

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Auto deep learning for bioacoustic signals

https://github.com/google-research/chirp

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Global birdsong embeddings enable superior transfer learning for bioacoustic classification

https://github.com/google-research/google-research/tree/master/vatt

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VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

https://github.com/google-research/leaf-audio

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LEAF: A Learnable Frontend for Audio Classification

https://github.com/google-research/perch

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Global birdsong embeddings enable superior transfer learning for bioacoustic classification

https://github.com/google-research/perch/tree/main/chirp

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Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics

https://github.com/google-research/scenic

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Audiovisual Masked Autoencoders

https://github.com/google-research/scenic/tree/main/scenic/projects/av_mae

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Audiovisual Masked Autoencoders

https://github.com/google-research/scenic/tree/main/scenic/projects/mbt

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Attention Bottlenecks for Multimodal Fusion

https://github.com/grigorisg9gr/polynomial_nets

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$Π-$nets: Deep Polynomial Neural Networks

https://github.com/grigorisg9gr/polynomials-for-augmenting-nns

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Augmenting Deep Classifiers with Polynomial Neural Networks

https://github.com/habla-liaa/ast-pe

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Study of positional encoding approaches for Audio Spectrogram Transformers

https://github.com/haoheliu/diffres-python

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Learning Temporal Resolution in Spectrogram for Audio Classification

https://github.com/haoyu-r/how-to-manage-tinyml-at-scale

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On-device Online Learning and Semantic Management of TinyML Systems

https://github.com/hsivan/fosi

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FOSI: Hybrid First and Second Order Optimization

https://github.com/ilaria-manco/mulap

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Learning music audio representations via weak language supervision

https://github.com/ilyassmoummad/scl_icbhi2017

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Pretraining Respiratory Sound Representations using Metadata and Contrastive Learning

https://github.com/inesnolas/Rank-based-loss_ICASSP22

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Rank-based loss for learning hierarchical representations

https://github.com/jambo6/generalised_shapelets

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Generalised Interpretable Shapelets for Irregular Time Series

https://github.com/jinhualiang/apt

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Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities

https://github.com/jiufengsc/elasticast

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ElasticAST: An Audio Spectrogram Transformer for All Length and Resolutions

https://github.com/jonahanton/ssl_audio

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Audio Barlow Twins: Self-Supervised Audio Representation Learning

https://github.com/jongsuk1/equiav

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EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning

https://github.com/juhannam/deepbof

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A Deep Bag-of-Features Model for Music Auto-Tagging

https://github.com/julirao/whisper_audio_classification

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Investigating the Emergent Audio Classification Ability of ASR Foundation Models

https://github.com/justinsalamon/UrbanSound8K-JAMS

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Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

https://github.com/k-h-ismail/dcls-audio

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Audio classification with Dilated Convolution with Learnable Spacings

https://github.com/kaen2891/adversarial_fine-tuning_using_generated_respiratory_sound

공식 코드

Adversarial Fine-tuning using Generated Respiratory Sound to Address Class Imbalance

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