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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
End-to-end Networks for Supervised Single-channel Speech Separation
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation
The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines
TasNet: time-domain audio separation network for real-time, single-channel speech separation
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
Using Optimal Ratio Mask as Training Target for Supervised Speech Separation
Supervised Speech Separation Based on Deep Learning: An Overview
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Speaker-independent Speech Separation with Deep Attractor Network
Multi-talker Speech Separation with Utterance-level Permutation Invariant Training of Deep Recurrent Neural Networks
Deep attractor network for single-microphone speaker separation
Deep Clustering and Conventional Networks for Music Separation: Stronger Together
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models
Single-Channel Multi-Speaker Separation using Deep Clustering
Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses
Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge
Deep clustering: Discriminative embeddings for segmentation and separation
Joint Tensor Factorization and Outlying Slab Suppression with Applications
Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Deep learning for monaural speech separation
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