Development Of An Automatic Matching Dual-Microphone Noise Reduction System Utilizing Tms320c6713

Previously studies indicated that the adaptive directional microphone strategy has the characteristics of low computing cost and effective noise reduction. However, when two microphones were mismatched, the received signals showed differences on their phases and amplitudes. These differences would decrease the noise reduction performance if this mismatch was not well compensated. The purpose of this study was to develop an auto-matching process to match the dual-microphones and improve the performance of automatic scene classification noise reduction system before the application of the adaptive directional microphone strategy. In this study, the auto-matching algorithms were implemented in TMS320C6713 and compared with the mismatched dual-microphone in the original noise reduction system. The speech reception thresholds (SRTs) from eight normal hearing subjects in different noise conditions were measured with the HINT Pro system for subjective evaluation. The results showed that the automatic scene classification noise reduction system provided significant SRT effect and had better speech intelligibility. The perceptual evaluation of speech quality (PESQ) was further used to estimate the quality of speech. Our results showed that the auto-matching dual-microphone system provides more speech quality than those of the original dual-microphone system when the signal-to-noise ratio (SNR) is above 15dB. The PESQ index indicated less distortion of original signals with the auto-matching system. When the SNR is below 15dB, the auto-matching dual-microphone system could discriminate accurately the noise type and decrease the speech distortion caused by the automatic scene classification noise reduction system. These results suggested that auto-matching system not only improve speech intelligibility but also let automatic scene classification noise reduction system obtain more accurate results.