Adaptive blind audio source extraction supervised by dominant speaker identification using x-vectors
Audio and Speech Processing
2019-10-28 v1 Sound
Signal Processing
Abstract
We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The algorithm is partially supervised by a pilot signal related to the source of interest (SOI), which ensures that the method correctly extracts the utterance of the desired speaker. The pilot is based on the identification of a dominant speaker in the mixture using x-vectors. The properties of the x-vectors computed in the presence of cross-talk are experimentally analyzed. The proposed approach is verified in a scenario with a moving SOI, static interfering speaker, and environmental noise.
Cite
@article{arxiv.1910.11824,
title = {Adaptive blind audio source extraction supervised by dominant speaker identification using x-vectors},
author = {Jakub Janský and Jiří Málek and Jaroslav Čmejla and Tomáš Kounovský and Zbyněk Koldovský and Jindřich Žďánský},
journal= {arXiv preprint arXiv:1910.11824},
year = {2019}
}