Phase Processing for Single‐Channel Source Separation

Mowlaee, Pejman; Mayer, Florian; Kulmer, Josef; Stahl, Johannes

doi:10.1002/9781119238805.ch5

Cited by 8 publications

(5 citation statements)

References 57 publications

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“…The phase of speech mixture is used to combine with predicted magnitude to reconstruct waveforms. Currently, more researchers realized the negative influence of waveform reconstruction using mixture phase and started to work on phase modeling and retrieving [18,15,16,14]. For example, under an extreme condition when the mixture phase is opposite to the oracle phase, even though the magnitude is perfectly predicted, the reconstructed waveform is far away from the ground truth [17].…”

Section: Introductionmentioning

confidence: 99%

End-to-End Multi-Channel Speech Separation

Gu,

Wu,

Zhang

et al. 2019

Preprint

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The end-to-end approach for single-channel speech separation has been studied recently and shown promising results. This paper extended the previous approach and proposed a new endto-end model for multi-channel speech separation. The primary contributions of this work include 1) an integrated waveform-in waveform-out separation system in a single neural network architecture. 2) We reformulate the traditional short time Fourier transform (STFT) and inter-channel phase difference (IPD) as a function of time-domain convolution with a special kernel.3) We further relaxed those fixed kernels to be learnable, so that the entire architecture becomes purely data-driven and can be trained from end-to-end. We demonstrate on the WSJ0 farfield speech separation task that, with the benefit of learnable spatial features, our proposed end-to-end multi-channel model significantly improved the performance of previous end-to-end single-channel method and traditional multi-channel methods.

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Section: Introductionmentioning

confidence: 99%

End-to-End Multi-Channel Speech Separation

Gu,

Wu,

Zhang

et al. 2019

Preprint

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“…RELATION TO PRIOR WORKS Recently, there has been increasing interest in phase-aware speech enhancement because of the sub-optimality of reusing the phase of the mixture signal. While most of these works tried to estimate the clean phase by using a phase mask or an additional network, the absolute phase difference between mixture and source can be actually computed using the law of cosines [31]. Inspired by this, [6] proposed to estimate a rotational direction of the absolute phase difference for speech separation.…”

Section: Listening Test Resultsmentioning

confidence: 99%

Real-Time Denoising and Dereverberation wtih Tiny Recurrent U-Net

Choi

Park

Lee³

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Modern deep learning-based models have seen outstanding performance improvement with speech enhancement tasks. The number of parameters of state-of-the-art models, however, is often too large to be deployed on devices for real-world applications. To this end, we propose Tiny Recurrent U-Net (TRU-Net), a lightweight online inference model that matches the performance of current state-ofthe-art models. The size of the quantized version of TRU-Net is 362 kilobytes, which is small enough to be deployed on edge devices. In addition, we combine the small-sized model with a new masking method called phase-aware β-sigmoid mask, which enables simultaneous denoising and dereverberation. Results of both objective and subjective evaluations have shown that our model can achieve competitive performance with the current state-of-the-art models on benchmark datasets using fewer parameters by orders of magnitude.

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“…However, recent studies [32] show the importance of the accurate phase as it can significantly improve perceptual speech quality, especially in low SNR conditions. Subsequently, some SE algorithms [33,34] are developed to solve this problem, and consistently show objective speech quality improvements when the phase is enhanced.…”

Section: Deep Complex Neural Network Based For Phase-aware Sementioning

confidence: 99%

A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement

Wang

et al. 2021

Preprint

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Cycle-consistent generative adversarial networks (CycleGAN) have shown their promising performance for speech enhancement (SE), while one intractable shortcoming of these CycleGAN-based SE systems is that the noise components propagate throughout the cycle and cannot be completely eliminated.Additionally, conventional CycleGAN-based SE systems only estimate the spectral magnitude, while the phase is unaltered. Motivated by the multi-stage learning concept, we propose a novel two-stage denoising system that combines a CycleGAN-based magnitude enhancing network and a subsequent complex spectral refining network in this paper. Specifically, in the first stage, a CycleGAN-based model is responsible for only estimating magnitude, which is subsequently coupled with the original noisy phase to obtain a coarsely enhanced complex spectrum. After that, the second stage is applied to further suppress the residual noise components and estimate the clean phase by a complex spectral mapping network, which is a pure complex-valued network composed of complex 2D convolution/deconvolution and complex temporal-frequency atten-

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Phase Processing for Single‐Channel Source Separation

Cited by 8 publications

References 57 publications

End-to-End Multi-Channel Speech Separation

End-to-End Multi-Channel Speech Separation

Real-Time Denoising and Dereverberation wtih Tiny Recurrent U-Net

A Two-stage Complex Network using Cycle-consistent Generative Adversarial Networks for Speech Enhancement

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