Algorithm for Independent Vector Extraction Based on Semi-Time-Variant Mixing Model

TL;DR

This paper introduces a fast, semi-time-variant mixing model-based algorithm for dynamic independent vector extraction, optimized via a quasi-likelihood contrast function, demonstrating superior online speech extraction performance.

Contribution

It presents a novel algorithm that combines semi-time-variant modeling with a quasi-likelihood approach, improving speed and stability over existing methods.

Findings

Faster convergence than gradient-based algorithms.

Effective in online speech extraction with dense microphone arrays.

Enforces orthogonality post-update for efficiency.

Abstract

A new algorithm for dynamic independent vector extraction is proposed. It is based on the mixing model where mixing parameters related to the source-of-interest (SOI) are time-variant while the separating parameters are time-invariant. A contrast function based on the quasi-likelihood approach is optimized using the Newton-Raphson approach. The update is computed without imposing the orthogonal constraint, and the orthogonality is enforced afterward. This yields an algorithm that is significantly faster than gradient-based algorithms while different from fixed-point methods, which are even faster. We show advantageous properties of the proposed algorithm compared to the fixed-point methods in an on-line processing regime where stable convergence to the SOI is the important issue. The effectiveness of the method is demonstrated in a speech extraction experiment with a dense microphone array.