Single-channel Speech Separation Based on Separated SI-SNR Regression Estimation and Adaptive Frequency Modulation Network

Zhang Rui; Lyu Jun

doi:10.12052/gdutxb.210149

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Single-channel Speech Separation Based on Separated SI-SNR Regression Estimation and Adaptive Frequency Modulation Network

更新时间：2026-05-27

- Single-channel Speech Separation Based on Separated SI-SNR Regression Estimation and Adaptive Frequency Modulation Network
- Vol. 40, Issue 2, Pages: 45-54(2023)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210149
  CLC： TP301
- Online First：25 March 2023，
  
  Published：2023
- 稿件说明：
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张锐, 吕俊. 基于分离结果信噪比估计与自适应调频网络的单通道语音分离技术[J]. 广东工业大学学报, 2023, 40(2): 45-54.

Zhang Rui, Lyu Jun. Single-channel Speech Separation Based on Separated SI-SNR Regression Estimation and Adaptive Frequency Modulation Network[J]. 2023, 40(2): 45-54.
张锐, 吕俊. 基于分离结果信噪比估计与自适应调频网络的单通道语音分离技术[J]. 广东工业大学学报, 2023, 40(2): 45-54. DOI： 10.12052/gdutxb.210149.

Zhang Rui, Lyu Jun. Single-channel Speech Separation Based on Separated SI-SNR Regression Estimation and Adaptive Frequency Modulation Network[J]. 2023, 40(2): 45-54. DOI： 10.12052/gdutxb.210149.

摘要

在实际应用中，语音分离模型往往受到未知噪声的干扰，从而出现泛化性能严重退化的问题。据此本文提出了基于分离结果信噪比估计与自适应调频网络的单通道语音分离方法。该方法首先通过预测网络对测试信号分离结果的尺度不变信噪比进行估计，以此计算模型的认知不确定性；然后，设计自适应调频网络针对不确定性较高的信号进行自适应频谱调节，以降低模型认知不确定性，从而提升模型在面对未知噪声时的泛化能力。实验结果表明：本文提出的方法相比于单独的时域卷积语音分离网络，将SI-SNR指标从2.72 dB提升至4.57 dB，增幅达到67.94%，在泛化能力上具有较大的改善；相比于增加了软掩膜过滤机制的时域卷积语音分离网络，将SI-SNR指标从3.32 dB提升至4.57 dB，增幅达到37.65%，表明该方法在提高泛化能力方面的能力优于软掩膜过滤机制。

Abstract

In practical applications

speech separation models are often disturbed by unknown noise

resulting in serious degradation of generalization performance. To solve this problem

Single channel speech separation method based on separate SNR regression estimation and adaptive frequency modulation network is proposed. Firstly

the scale invariant SNR of test signal separation results is estimated by prediction network to calculate the cognitive uncertainty of the model; Then

an adaptive frequency modulation network is designed to adjust the spectrum of signals with high uncertainty to reduce the cognitive uncertainty of the model

so as to improve the generalization ability of the model in the face of unknown noise. The experimental results show that compared with the Conv-Tasnet

the proposed method improves the SI-SNR (Scale Invariant SNR) from 2.72 dB to 4.57 dB

with an increase of 67.94%

and has a great improvement in generalization ability. Compared with Conv-Tasnet with Soft-Mask

the SI-SNR is increased from 3.32 dB to 4.57 dB

with an increase of 37.65%

indicating that this method has better generalization ability than soft mask mechanism. It effectively alleviates the serious degradation of generalization ability of speech separation network in the face of unknown noise.

关键词

Keywords

references

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