Adaptive blind audio source extraction supervised by dominant speaker identification using x-vectors

Autor:	Janský, Jakub, Málek, Jiří, Čmejla, Jaroslav, Kounovský, Tomáš, Koldovský, Zbyněk, Žďánský, Jindřich
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.11824 Zobrazit plný text záznamu View this record from Arxiv