TY - GEN
T1 - Development of a speech enhancement dual-microphone noise reduction system utilizing TMS320C6713
AU - Wu, Chao Min
AU - Chen, Zeng Fong
AU - Liu, Ting An
PY - 2016
Y1 - 2016
N2 - The purpose of this research was to add a speech enhancement process that could further improve speech intelligibility and the performance of automatic scene classification and automatching noise reduction system after the application of the adaptive directional microphone strategy. The speech enhancement system is divided into two parts: one is the noise-estimation strategy and the other the speech-estimation function. Noise-estimation algorithms used in the research are: minimum statistics (MS), minima-controlled recursive averaging (MCRA), improved(IMCRA), MCRA-Loizou (MCRA-L), constrained variance spectral smoothing (CVS), forward-backward MCRA(MCRA-FB);speech-estimation function: Wiener filter, maximum-likelihood (ML), log-spectral amplitude (LSA), maximum a posteriori amplitude (MAPA). In this research, The MATLAB program was first used to simulate the speech enhancement system to evaluate the quality of output speech signal under different signal-to-noise ratio (SNR) conditions, and then to select the best combination of the speech enhancement system. Finally, the selected speech enhancement system was implemented with automatic scene classification and auto-matching noise reduction system in TMS320C6713 DSP Starter Kit (Texas Instruments, Dallas, Texas, USA), and compared with the output signal in the original noise reduction system. To show the performance of the selected speech enhancement system, the objective perceptual evaluation of speech quality (PESQ) approach was further used to estimate the quality of speech with the SNR range from +30 and -30dB. Our simulated results showed that the PESQ score was increased by 0.45 when the speech enhancement CVS with MAPA was used for the input signal with 30 dB SNR and by 0.65 for 10 dB SNR. However, for the hardware implementation, only the speech enhancement MCRA with MAPA was used for real-time processing. The PESQ score was increased by 0.27 for the input signal with 0 dB SNR. With the speech enhancement system, our overall hardware implementation could effectively reduce speech distortion and improve speech intelligibility.
AB - The purpose of this research was to add a speech enhancement process that could further improve speech intelligibility and the performance of automatic scene classification and automatching noise reduction system after the application of the adaptive directional microphone strategy. The speech enhancement system is divided into two parts: one is the noise-estimation strategy and the other the speech-estimation function. Noise-estimation algorithms used in the research are: minimum statistics (MS), minima-controlled recursive averaging (MCRA), improved(IMCRA), MCRA-Loizou (MCRA-L), constrained variance spectral smoothing (CVS), forward-backward MCRA(MCRA-FB);speech-estimation function: Wiener filter, maximum-likelihood (ML), log-spectral amplitude (LSA), maximum a posteriori amplitude (MAPA). In this research, The MATLAB program was first used to simulate the speech enhancement system to evaluate the quality of output speech signal under different signal-to-noise ratio (SNR) conditions, and then to select the best combination of the speech enhancement system. Finally, the selected speech enhancement system was implemented with automatic scene classification and auto-matching noise reduction system in TMS320C6713 DSP Starter Kit (Texas Instruments, Dallas, Texas, USA), and compared with the output signal in the original noise reduction system. To show the performance of the selected speech enhancement system, the objective perceptual evaluation of speech quality (PESQ) approach was further used to estimate the quality of speech with the SNR range from +30 and -30dB. Our simulated results showed that the PESQ score was increased by 0.45 when the speech enhancement CVS with MAPA was used for the input signal with 30 dB SNR and by 0.65 for 10 dB SNR. However, for the hardware implementation, only the speech enhancement MCRA with MAPA was used for real-time processing. The PESQ score was increased by 0.27 for the input signal with 0 dB SNR. With the speech enhancement system, our overall hardware implementation could effectively reduce speech distortion and improve speech intelligibility.
UR - http://www.scopus.com/inward/record.url?scp=84987858627&partnerID=8YFLogxK
M3 - 會議論文篇章
AN - SCOPUS:84987858627
T3 - ICSV 2016 - 23rd International Congress on Sound and Vibration: From Ancient to Modern Acoustics
BT - ICSV 2016 - 23rd International Congress on Sound and Vibration
PB - International Institute of Acoustics and Vibrations
T2 - 23rd International Congress on Sound and Vibration, ICSV 2016
Y2 - 10 July 2016 through 14 July 2016
ER -