Development Of A Speech Enhancement Dual Microphone Noise Reduction System Utilizing Tms320c6713

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 auto matching 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.