Speaker Separation

11 papers with code • 0 benchmarks • 3 datasets

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Most implemented papers

Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation

naplab/Conv-TasNet 20 Sep 2018

The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms.

VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking

Edresson/VoiceSplit 11 Oct 2018

In this paper, we present a novel system that separates the voice of a target speaker from multi-speaker signals, by making use of a reference signal from the target speaker.

Single-Channel Multi-Speaker Separation using Deep Clustering

JusperLee/Deep-Clustering-for-Speech-Separation 7 Jul 2016

In this paper we extend the baseline system with an end-to-end signal approximation objective that greatly improves performance on a challenging speech separation.

Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation

yuhogun0908/MISOnet 4 Oct 2020

Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.

Deep attractor network for single-microphone speaker separation

KMASAHIRO/DANet 27 Nov 2016

We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source.

Monaural Audio Speaker Separation with Source Contrastive Estimation

lab41/magnolia 12 May 2017

Although the matrix determined by the output weights is dependent on a set of known speakers, we only use the input vectors during inference.

Neural separation of observed and unobserved distributions

tavihalperin/Neural-Egg-Seperation ICLR 2019

In this work, we introduce a new method---Neural Egg Separation---to tackle the scenario of extracting a signal from an unobserved distribution additively mixed with a signal from an observed distribution.

Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation

yuzhou-git/deep-casa 25 Apr 2019

Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network.

Speech Separation Based on Multi-Stage Elaborated Dual-Path Deep BiLSTM with Auxiliary Identity Loss

ShiZiqiang/dual-path-RNNs-DPRNNs-based-speech-separation 6 Aug 2020

We have open sourced our re-implementation of the DPRNN-TasNet here (https://github. com/ShiZiqiang/dual-path-RNNs-DPRNNs-based-speech-separation), and our TasTas is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be reproduced with ease.

Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training

SungFeng-Huang/SSL-pretraining-separation 29 Oct 2020

Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired.