Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation
This work offers a computationally efficient solution for blind source separation in complex acoustic environments, which is beneficial for applications like speech recognition and hearing aids.
This paper tackles blind source separation in noisy, reverberant environments with many microphones by integrating weighted prediction error (WPE) dereverberation with independent vector extraction (IVE). The proposed method achieves significantly faster convergence compared to a conventional WPE and independent vector analysis approach, while maintaining comparable separation performance.
We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones $M$ is greater than the number of sources of interest, and the other noise components can be approximated as stationary and Gaussian distributed. Conventional BSS algorithms for the optimization of a multi-input multi-output convolutional beamformer have suffered from a huge computational cost when $M$ is large. We here propose a computationally efficient method that integrates a weighted prediction error (WPE) dereverberation method and a fast BSS method called independent vector extraction (IVE), which has been developed for less reverberant environments. We show that, given the power spectrum for each source, the optimization problem of the new method can be reduced to that of IVE by exploiting the stationary condition, which makes the optimization easy to handle and computationally efficient. An experiment of speech signal separation shows that, compared to a conventional method that integrates WPE and independent vector analysis, our proposed method achieves much faster convergence while maintaining its separation performance.