Papers with Code 아카이브
Audio Classification
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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/afperezm/acoustic-images-distillation
공식 코드https://github.com/ahmedheakl/Study-on-Broadcast-Networks-for-Music-Genre-Classification
공식 코드A Study on Broadcast Networks for Music Genre Classification
https://github.com/ai-techsystems/deepC
공식 코드Tiny Transformers for Environmental Sound Classification at the Edge
https://github.com/alexandrosstergiou/PlayItBack
공식 코드https://github.com/ali-vosoughi/counterfactual-audio
공식 코드Learning Audio Concepts from Counterfactual Natural Language
https://github.com/alireza-nasiri/SoundCLR
공식 코드SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification
https://github.com/amu-cai/nemo
공식 코드https://github.com/andylee-24/token-pruning-audio-transformer
공식 코드Token Pruning in Audio Transformers: Optimizing Performance and Decoding Patch Importance
https://github.com/ankitshah009/WALNet-Weak_Label_Analysis
공식 코드https://github.com/apple/corenet
공식 코드Bytes Are All You Need: Transformers Operating Directly On File Bytes
https://github.com/apple/ml-cvnets
공식 코드Bytes Are All You Need: Transformers Operating Directly On File Bytes
https://github.com/atriantafyllopoulos/audiocub-zsl
공식 코드Exploring Meta Information for Audio-based Zero-shot Bird Classification
https://github.com/audio-westlakeu/audiossl
공식 코드Self-supervised Audio Teacher-Student Transformer for Both Clip-level and Frame-level Tasks
https://github.com/bbc/bbc-speech-segmenter
공식 코드X-Vector based voice activity detection for multi-genre broadcast speech-to-text
https://github.com/cheggan/metaaudio-a-few-shot-audio-classification-benchmark
공식 코드https://github.com/chenhao1umbc/WSCDL
공식 코드Weakly Supervised Convolutional Dictionary Learning for Multi-Label Classification
https://github.com/chumingqian/CycleGuardian
공식 코드https://github.com/clovaai/AdamP
공식 코드AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights
https://github.com/corticph/MSTmodel
공식 코드https://github.com/cpjku/efficientleaf
공식 코드EfficientLEAF: A Faster LEarnable Audio Frontend of Questionable Use
https://github.com/croitorualin/lerac
공식 코드https://github.com/cruvadom/Convolutional-RNN
공식 코드Convolutional RNN: an Enhanced Model for Extracting Features from Sequential Data
https://github.com/curlsloth/MusicSpeech-STM
공식 코드https://github.com/cwx-worst-one/eat
공식 코드EAT: Self-Supervised Pre-Training with Efficient Audio Transformer
https://github.com/dbd-research-group/birdset
공식 코드BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics
https://github.com/deegy666/add-rsc
공식 코드Adaptive Differential Denoising for Respiratory Sounds Classification
https://github.com/deep-spin/sparse_continuous_distributions
공식 코드https://github.com/deepmind/brave
공식 코드https://github.com/deepmind/deepmind-research/tree/master/mmv
공식 코드https://github.com/deepmind/deepmind-research/tree/master/perceiver
공식 코드https://github.com/dkurzend/clipclap-gzsl
공식 코드Audio-Visual Generalized Zero-Shot Learning using Pre-Trained Large Multi-Modal Models
https://github.com/eacevedo1/audiotext-contextda
공식 코드https://github.com/edac-ml4h/edac-ml4h
공식 코드EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection
https://github.com/ekazakos/auditory-slow-fast
공식 코드https://github.com/facebookresearch/AVID-CMA
공식 코드Audio-Visual Instance Discrimination with Cross-Modal Agreement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.