混响环境下基于卷积模型的欠定盲源分离

Underdetermined Blind Source Separation Based on Convolution Model in Reverberant Environment

为了提高盲源分离(blind source separation, BSS)算法在混响和噪声环境下的鲁棒性,提出了一种适用于欠定情况下用于卷积混合信号的盲源分离算法。在该算法中,利用高混响环境下混合模型即使在时频(time-frequency, TF)域中仍具有卷积特性,并结合房间冲激响应(room impulse response, RIR)的统计规律,将时频域中的瞬时模型扩展到更适合高混响环境的卷积模型,进而构建一欠定盲源分离优化问题,最后,采用交替方向乘子法(alternating direction method of multipliers, ADMM)优化框架来求解该问题。仿真实验结果表明,在混响环境下,本文提出的基于卷积模型的盲源分离算法与现有盲源分离算法相比,具有非常明显的性能优势。

To improve the robustness of the blind source separation algorithm in the presence of reverberation and noise, a blind source separation method for convolutional mixed signals under underdetermined conditions is proposed in this work. Due to the high reverberation, the mixing model presents convolution property even in the time-frequency(TF) domain. By using the characteristics of room impulse response, the instantaneous model in the TF domain is extended to a convolution model is more suitable for high reverberation environments, Based on the convolution model in the TF domain, a joint optimization problem of blind source separation is developed, and the resulting optimization problem is solved via an alternating optimization framework where the alternating direction method of multipliers(ADMM) is devised. The results of simulation experiments show that the performance of the convolution model in the reverberation environment outperforms the instantaneous model, which verifies the effectiveness of the convolution model in the TF domain.