An adaptive Speech Denoising System based on ICA with Voice Activity Detection

This contribution presents an innovative system for adaptive speech denoising using Independent Component Analysis (ICA) and Voice Activity Detection (VAD) in low dB-SNR environments. The implemented experiments consider instantaneous mixtures (two sources and two microphones) where the proposed system identifies the noise contained in each noisy mixture, applies the most suitable block ICA method among 3 methods (FastICA, Kernel ICA and JADE) and, after source separation, automatically identifies the estimated speech signal. The ICA suitability is in accordance with the detected noise, the signal mixtures are non-linear and the proposed system extracts information that can be used for further pre and/or postprocessing and for improving the block ICA’s output. The process is completely automatic from the source recording to its output and such system has a wide range of applications and significant potential over the conventional approaches.報告番号: ; 学位授与年月日: 2008-03-24 ; 学位の種別: 修士 ; 学位の種類: 修士(情報理工学) ; 学位記番号: ; 研究科・専攻: 情報理工学系研究科電子情報学専

独立成分分析と音声区間検出による雑音除去

This contribution presents an innovative system for adaptive speech denoising using Independent Component Analysis (ICA) and Voice Activity Detection (VAD) in low dB-SNR environments. The implemented experiments consider instantaneous mixtures (two sources and two microphones) where the proposed system identifies the noise contained in each noisy mixture, applies the most suitable block ICA method among 3 methods (FastICA, Kernel ICA and JADE) and, after source separation, automatically identifies the estimated speech signal. The ICA suitability is in accordance with the detected noise, the signal mixtures are non-linear and the proposed system extracts information that can be used for further pre and/or postprocessing and for improving the block ICA’s output. The process is completely automatic from the source recording to its output and such system has a wide range of applications and significant potential over the conventional approaches