Signal processing methods for enhancing speech and music signals in reverberant environments

• Κύριος συγγραφέας: Τσιλφίδης, Αλέξανδρος
• Άλλοι συγγραφείς: Μουρτζόπουλος, Ιωάννης
• Μορφή: Thesis
• Γλώσσα: English
• Έκδοση: 2011
• Θέματα: Digital signal processing; Room accoustics; Psychoacoustics; Dereverberation; Noise suppression; Automatic speech recognition; Speech processing; Music processing; Ψηφιακή επεξεργασία σήματος; Ακουστική χώρων; Ψυχοακουστική; Αφαίρεση αντήχησης; Αφαίρεση ακουστικού θορύβου; Αυτόματη αναγνώριση ομιλίας; Επεξεργασία μουσικής; 621.382 24

This thesis presents novel signal processing algorithms for speech and music dereverberation. The proposed algorithms focus on blind single-channel suppression of late reverberation; however binaural and semi-blind methods have also been introduced. Late reverberation is a particularly harmful distortion, since it significantly decreases the perceived quality of the reverberant signals but also degrades the performance of Automatic Speech Recognition (ASR) systems and other speech and music processing algorithms. Hence, the proposed deverberation methods can be either used as standalone enhancing techniques or implemented as preprocessing schemes prior to ASR or other applied systems. The main dereverberation method proposed here is a blind dereverberation technique based on perceptual reverberation modeling has been developed. This technique employs a computational auditory masking model and locates the signal regions where late reverberation is audible, i.e. where it is unmasked from the clean signal components. Following a selective signal processing approach, only such signal regions are further processed through sub-band gain filtering. The above technique has been evaluated for both speech and music signals and for a wide range of reverberation conditions. In all cases it was found to minimize the processing artifacts and to produce perceptually superior clean signal estimations than any other tested technique. Moreover, extensive ASR tests have shown that it significantly improves the recognition performance, especially in highly reverberant environments.