Estimation of Relative Transfer Function in the Presence of Stationary Noise Based on Segmental Power Spectral Density Matrix Subtraction - Archive ouverte HAL Access content directly
Conference Papers Year : 2015

Estimation of Relative Transfer Function in the Presence of Stationary Noise Based on Segmental Power Spectral Density Matrix Subtraction

Abstract

This paper addresses the problem of relative transfer function (RTF) estimation in the presence of stationary noise. We propose an RTF identification method based on segmental power spectral density (PSD) matrix subtraction. First multiple channel microphone signals are divided into segments corresponding to speech-plus-noise activity and noise-only. Then, the subtraction of two segmental PSD matrices leads to an almost noise-free PSD matrix by reducing the stationary noise component and preserving non-stationary speech component. This noise-free PSD matrix is used for single speaker RTF identification by eigenvalue decomposition. Experiments are performed in the context of sound source localization to evaluate the efficiency of the proposed method.
Fichier principal
Vignette du fichier
xiaofei_interference.pdf (719.56 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01119186 , version 1 (21-02-2015)

Identifiers

Cite

Xiaofei Li, Laurent Girin, Radu Horaud, Sharon Gannot. Estimation of Relative Transfer Function in the Presence of Stationary Noise Based on Segmental Power Spectral Density Matrix Subtraction. ICASSP 2015 - 40th IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2015, Brisbane, Australia. pp.320 - 324, ⟨10.1109/ICASSP.2015.7177983⟩. ⟨hal-01119186⟩
1439 View
716 Download

Altmetric

Share

Gmail Facebook X LinkedIn More