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Audio Classification

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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/habla-liaa/encodecmae

Leveraging Pre-Trained Autoencoders for Interpretable Prototype Learning of Music Audio

https://github.com/harritaylor/torchvggish

CNN Architectures for Large-Scale Audio Classification

https://github.com/idiap/sparch

https://github.com/iver56/audiomentations

AudioCLIP: Extending CLIP to Image, Text and Audio

https://github.com/jaron/deep-listening

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

https://github.com/jesperhauch/polynomial_deep_learning

Augmenting Deep Classifiers with Polynomial Neural Networks

https://github.com/k-h-ismail/dilated-convolution-with-learnable-spacings-pytorch

Audio classification with Dilated Convolution with Learnable Spacings

https://github.com/kietngt00/hmdb51-recognition

Perceiver: General Perception with Iterative Attention

https://github.com/kkoutini/passt_hear21

Efficient Training of Audio Transformers with Patchout

https://github.com/krasserm/perceiver-io

Perceiver: General Perception with Iterative Attention

https://github.com/kzkadc/poly-nets

$Π-$nets: Deep Polynomial Neural Networks

https://github.com/levtelyatnikov/radiomixer

Artificially Synthesising Data for Audio Classification and Segmentation to Improve Speech and Music Detection in Radio Broadcast

https://github.com/louislva/deepmind-perceiver

Perceiver: General Perception with Iterative Attention

https://github.com/lucidrains/perceiver-pytorch

Perceiver: General Perception with Iterative Attention

https://github.com/makcedward/nlpaug

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

https://github.com/mariostrbac/environmental-sound-classification

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

https://github.com/marl/l3embedding

Look, Listen and Learn

https://github.com/modelscope/modelscope

ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities

https://github.com/morrisxu-driving/video-music_cross-modal_retrival

CNN Architectures for Large-Scale Audio Classification

https://github.com/namakemono/kaggle-alaska2-image-steganalysis

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

https://github.com/npvoid/SparseSpikingBackprop

Sparse Spiking Gradient Descent

https://github.com/nttcslab/composing-general-audio-repr

CNN Architectures for Large-Scale Audio Classification

https://github.com/numediart/MAFnet

CNN Architectures for Large-Scale Audio Classification

https://github.com/phuriches/genrepasd

BEATs: Audio Pre-Training with Acoustic Tokenizers

https://github.com/pku-yuangroup/video-bench

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

https://github.com/porcelluscavia/audio-model

Slow-Fast Auditory Streams For Audio Recognition

https://github.com/pwc-1/Paper-8/tree/main/audio_spectrogram_transformer

AST: Audio Spectrogram Transformer

https://github.com/pwc-1/Paper-9/tree/main/3/vat

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

https://github.com/pxaris/ccml

AST: Audio Spectrogram Transformer

https://github.com/qingyuliu0521/icsd

BEATs: Audio Pre-Training with Acoustic Tokenizers

https://github.com/rickyHong/Conv-RNN-repl

Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data

https://github.com/rishikksh20/AudioMAE-pytorch

MAE-AST: Masked Autoencoding Audio Spectrogram Transformer

https://github.com/rwightman/pytorch-image-models

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

https://github.com/sajabdoli/UAP

Universal Adversarial Audio Perturbations

https://github.com/semantic-search/MAX-Audio-ClassifierX

CNN Architectures for Large-Scale Audio Classification

https://github.com/shenchenyi/SpeakerData

AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark

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