Convolutional Blind Source Separation Method Based on Impulse Response Compression

ZHANG Zhiwei; GAO Hongyuan; WANG Jiayi; LIU Yapeng

doi:10.12263/DZXB.20260468

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Convolutional Blind Source Separation Method Based on Impulse Response Compression

PAPERS | 更新时间：2026-10-16

- Convolutional Blind Source Separation Method Based on Impulse Response Compression
- ACTA ELECTRONICA SINICA Vol. 54, Issue 6, Pages: 2805-2818(2026)
- 作者机构：
  
  哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62372131);Natural Science Foundation of Heilongjiang Province(LH2020F017);Fundamental Research Funds for the Central Universities(3072025YC0801)
- DOI：10.12263/DZXB.20260468
  CLC： TN912.3;
- Received：27 May 2026，
  
  Accepted：02 June 2026，
  
  Published：25 June 2026
- 稿件说明：
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张志伟, 高洪元, 王佳仪, 等. 基于脉冲响应压缩的卷积盲源分离方法[J]. 电子学报, 2026, 54(06): 2805-2818.

ZHANG Zhiwei, GAO Hongyuan, WANG Jiayi, et al. Convolutional Blind Source Separation Method Based on Impulse Response Compression[J]. Acta Electronica Sinica, 2026, 54(06): 2805-2818.
张志伟, 高洪元, 王佳仪, 等. 基于脉冲响应压缩的卷积盲源分离方法[J]. 电子学报, 2026, 54(06): 2805-2818. DOI：10.12263/DZXB.20260468

ZHANG Zhiwei, GAO Hongyuan, WANG Jiayi, et al. Convolutional Blind Source Separation Method Based on Impulse Response Compression[J]. Acta Electronica Sinica, 2026, 54(06): 2805-2818. DOI：10.12263/DZXB.20260468

摘要

已有方法往往将加权预测误差（Weighted Prediction Error，WPE）去混响方法与独立成分分析（Independent Component Analysis，ICA）技术、独立向量分析（Independent Vector Analysis，IVA）技术结合以实现混响环境下的卷积盲源分离。然而，ICA和IVA没有构建灵活的声源模型，因此独立低秩矩阵分析（Independ

ent Low Rank Matrix Analysis，ILRMA）技术被提出，进而组成WPE与ILRMA的联合优化，即WPE-ILRMA。但是，WPE-ILRMA的性能依然依赖迭代次数和滤波器阶数设置，迭代次数较少时，方法性能较差，固定的滤波器阶数设置也无法应对复杂多变的混响环境，并且分离滤波器的更新依然需要对矩阵求逆，消耗硬件资源较大。利用梯度下降法优化预滤波器的脉冲响应重构（Impulse Response Remodeling，IRR）模型被提出以缩短脉冲响应长度和降低高混响环境的影响，但是这些方法基于IRR模型构建的目标函数需要设置范数值，范数值仅凭经验设置，设置不当易导致IRR效果下降，而在盲信号处理场景下，根据IRR效果不断调整目标函数的范数值是不实际的。针对现有卷积盲源分离方法性能依赖于范数值或者滤波器阶数设置的难题，首次将压缩变换概念引入IRR技术中，推导证明了压缩变换可以实现优于

范数的混响抑制性能，进而可以用压缩变换替代传统IRR模型中的

范数，创新性地设计了一种压缩变换与无穷范数相结合的脉冲响应压缩（Impulse Response Compression，IRC）模型，所提出的IRC模型既实现了更优越的混响抑制性能，又无需像传统去混响方法一样凭经验设置范数值或滤波器阶数以调节混响抑制性能。另外引入迭代源控制（Iterative Source Steering，ISS）技术与ILRMA结合，规避了矩阵求逆运算，使所提方法更易于硬件实现。仿真结果表明，在高混响环境下，不论是针对成分差异较小的动物声音信号分离问题、成分复杂的音乐信号分离问题，还是成分差异较大的语音信号分离问题，所提出的方法相比于其他方法均实现了更优越的信号分离性能，这表明了所提出方法拥有较强的鲁棒性，应用范围广泛。已有方法采用固定的范数值或滤波器阶数设置，参数设置不当会导致方法性能下降，而所提出方法的性能不依赖于范数值或滤波器阶数的调整。所提出的方法突破了已有方法的应用局限，并且IRC技术也可移植应用于其他与去混响相关的工程问题中。

Abstract

The existing methods often combine the weighted prediction error (WPE) de-reverberation method with independent component analysis (ICA) and independent vector analysis (IVA) techniques to realize convolutional blind source separation in a reverberation environment. However

ICA and IVA do not construct flexible sound source models

so the independent low rank matrix analysis (ILRMA) technique is proposed

which in turn constitutes the joint optimization of WPE and ILRMA

namely WPE-ILRMA. However

the performance of WPE-ILRMA still depends on the number of iterations and the setting of filter order. When the number of iterations is small

the performance of the method is poor

and the fixed filter order setting cannot cope with the complex and changeable reverberation environment. Moreover

the update of the separation filter still needs to invert the matrix

which consumes a lot of hardware resources. The impulse response remodeling (IRR) model using the gradient descent method to optimize the pre-filter is proposed to shorten the impulse respons

e length and reduce the influence of a high reverberation environment. However

the objective function Structured by these methods based on the IRR model needs to set the norm value

which is only set by experience. Improper settings will easily lead to the decrease of IRR effect. However

it is impractical to continuously adjust the norm value of the objective function according to the IRR effect in a blind signal processing scenario. Aiming at the problem that the performance of existing convolutional blind source separation methods depends on the norm value or filter order setting

the concept of compression transform is introduced into IRR technology for the first time. It is proven that the compression transform can achieve reverberation suppression performance better than

-norm

and then the compression transform can be used to replace

-norm in the traditional IRR model. An innovative impulse response compression (IRC) model combining the compression transform and the infinite norm is designed. The proposed IRC model not only achieves superior reverberation suppression performance

but also does not need to adjust reverberation suppression performance by empirically setting a norm value or filter order like traditional de-reverberation methods. In addition

the iterative source steering (ISS) technology and ILRMA are introduced to avoid the matrix inversion operation

which makes the proposed method easier to implement in hardware. The simulation results show that the proposed method achieves superior signal separation performance compared with other methods in a high reverberation environment

whether for the animal sound signal separation problem with small component difference

the music signal separation problem with complex components

or the speech signal separation problem with large component difference

which shows that the proposed method has strong robustness and a wide application range. The existing methods adopt a fixed norm value or filter order setting. Im

proper parameter setting will lead to the performance degradation of the method

but the performance of the proposed method does not depend on the adjustment of the norm value or filter order. The proposed method breaks through the application limitations of existing methods

and the IRC technology can also be applied to other engineering problems related to de-reverberation.

关键词

Keywords

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