Speech Separation
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The task of extracting all overlapping speech sources in a given mixed speech signal refers to the Speech Separation. Speech Separation is a special scenario of source separation problem, where the focus is only on the overlapping speech signal sources and other interferences such as music or noise signals are not the main concern of the study. A recent representative Github project can be referred to ClearerVoice-Studio.
Source: A Unified Framework for Speech Separation
Image credit: Speech Separation of A Target Speaker Based on Deep Neural Networks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/svoice
공식 코드Voice Separation with an Unknown Number of Multiple Speakers
https://github.com/fgnt/graph_pit
공식 코드https://github.com/francoisgrondin/steernet
공식 코드GEV Beamforming Supported by DOA-based Masks Generated on Pairs of Microphones
https://github.com/gnafiy/tpdt-ss-kws
공식 코드Text-aware Speech Separation for Multi-talker Keyword Spotting
https://github.com/griko/vanpy
공식 코드https://github.com/hangtingchen/Beam-Guided-TasNet
공식 코드Beam-Guided TasNet: An Iterative Speech Separation Framework with Multi-Channel Output
https://github.com/hmartelb/avlit
공식 코드Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
https://github.com/itsnotacie/sepprune
공식 코드SepPrune: Structured Pruning for Efficient Deep Speech Separation
https://github.com/iveveive/slnspeech
공식 코드CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language
https://github.com/joonaskalda/pixit
공식 코드https://github.com/jusperlee/ctcnet
공식 코드An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits
https://github.com/jusperlee/lrs3-for-speech-separation
공식 코드An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits
https://github.com/jusperlee/sonicsim
공식 코드SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios
https://github.com/jusperlee/spmamba
공식 코드SPMamba: State-space model is all you need in speech separation
https://github.com/jwr1995/dtcn
공식 코드Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation
https://github.com/jwr1995/pubsep
공식 코드https://github.com/jyhan03/icassp22-dataset
공식 코드https://github.com/karnwatcharasupat/directional-sparse-filtering-tf
공식 코드https://github.com/lixilinx/IVA4Cocktail
공식 코드Independent Vector Analysis with Deep Neural Network Source Priors
https://github.com/merlresearch/reverberation-as-supervision
공식 코드Enhanced Reverberation as Supervision for Unsupervised Speech Separation
https://github.com/merlresearch/tf-locoformer
공식 코드TF-Locoformer: Transformer with Local Modeling by Convolution for Speech Separation and Enhancement
https://github.com/mingyue66/textrolmix
공식 코드Beyond Speaker Identity: Text Guided Target Speech Extraction
https://github.com/modelscope/ClearerVoice-Studio
공식 코드https://github.com/mpariente/AsSteroid
공식 코드https://github.com/naplab/Conv-TasNet
공식 코드Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
https://github.com/nfurnon/disco
공식 코드Distributed speech separation in spatially unconstrained microphone arrays
https://github.com/r06944010/Speech-Separation-TF2
공식 코드Interrupted and cascaded permutation invariant training for speech separation
https://github.com/rrbluke/BSSD
공식 코드Blind Speech Separation and Dereverberation using Neural Beamforming
https://github.com/ruizhecao96/cmgan
공식 코드CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
https://github.com/shaharlutatipersonal/ocd
공식 코드https://github.com/shakeddovrat/librimix
공식 코드Many-Speakers Single Channel Speech Separation with Optimal Permutation Training
https://github.com/sinica-slam/casnet
공식 코드CasNet: Investigating Channel Robustness for Speech Separation
https://github.com/sinica-slam/cospro-mix
공식 코드Disentangling the Impacts of Language and Channel Variability on Speech Separation Networks
https://github.com/snuhcs/papez
공식 코드Papez: Resource-Efficient Speech Separation with Auditory Working Memory
https://github.com/sp-uhh/mp-gtf
공식 코드A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.