Abstract
Multi-channel speech coding and enhancement is an indispensable technology in speech communication. In order to verify the effectiveness of multi-channel speech coding and enhancement methods in the research and development, a microphone array speech simulator (MASS) used in room acoustic environment is proposed. The proposed MASS is the improvement and extension of the existing multi-channel speech simulator. It aims to simulate clean speech, noisy speech, clean speech with reverberation, noisy speech with reverberation, and noise signals by microphone array used for multi-channel coding and enhancement of speech signal in room acoustic environment. The experimental results of the multi-channel speech coding and enhancement prove that the MASS could well simulate the signals used in real room acoustic environment and can be applied to the research of the related fields.
Highlights
With the rapid development of signal processing and deep learning technology, more and more speech signal processing algorithms are proposed, which greatly promotes the progress of the speech processing, especially for multi-channel speech signal processing technology, such as multi-channel speech coding and multi-channel speech enhancement, because it can use the spatial information of speech signal, so as to obtain better processing effect than single-channel methods [1]
Microphone Array Speech Simulator is an improvement and extension of the existing pyroomacoustics, which exploits object-oriented of Python to createofathe clean and pyroomacoustics, intuitive application
The noisy speech microphone array with reverberation simulated bysimulated the three methods is used for training a convolutional dataset of microphone array with reverberation by the three methods is used for training neural network (CNN)-based multi-channel speech enhancement method
Summary
With the rapid development of signal processing and deep learning technology, more and more speech signal processing algorithms are proposed, which greatly promotes the progress of the speech processing, especially for multi-channel speech signal processing technology, such as multi-channel speech coding and multi-channel speech enhancement, because it can use the spatial information of speech signal, so as to obtain better processing effect than single-channel methods [1]. Zhang et al built an articulatory dataset specifying in Chinese Mandarin [9] and investigated its efficacy in speech animation, and the dataset was created by Carstens EMA AG501 device This real multi-channel speech data can be only used for specific structures but not for the simulation in any acoustic environment. Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) [13] proposed by Drude et al is a multi-channel dataset of overlapping speech for training, evaluation, and the detailed analysis of source separation and extraction It has a high degree of randomness w.r.t. room size, array center, and rotation, as well as speaker position. Through the analysis of isotropic spherically diffuse noise and set the corresponding signal-to-noise ratio (SNR) to better of the simulated speech room and the experiments on the multi-channel speech coding simulate real lifemicrophone scenes, sucharray as meeting acoustic environment.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
