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基于语音活动检测的阵列信号测向研究
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Abstract:
为研究长时信号中对具有特定特征的声音来源方向进行检测的问题，本课题提出一种基于多特征自适应的语音信号活动检测对长时阵列信号进行检测，将结合多子空间拟合(MUSIC)算法与语音活动检测(VAD)技术，提出一种新型的信号处理方法，旨在提高对特征明显且目标具有特定属性的信号源的检测精度和定位准确性。通过语音信号MFCC特征和语音信号能量特征来设置自适应阈值，对特定声源的特征进行语音活动检测，以提高语音活动检测的准确性。再通过检测到的语音信号活动片段进行阵列信号测向，通过MUSIC算法实现对长时信号中不同时段不同来源方向的特定声源进行检测。
To investigate the problem of detecting the direction of sound sources with specific features in long-term signals, this project proposes a voice signal activity detection method based on multi feature adaptation for detecting long-term array signals. By combining the Multi Subspace Fitting (MUSIC) algorithm with Voice Activity Detection (VAD) technology, a new signal processing method is proposed to improve the detection and localization accuracy of signal sources with obvious features and specific target attributes. By setting adaptive thresholds based on the MFCC features and energy features of voice signals, voice activity detection can be performed on specific sound source features to improve the accuracy of voice activity detection. Then, the direction of arrival is determined by detecting active voice signal segments, and the MUSIC algorithm is used to detect specific sound sources in different time periods and source directions in long-term signals.
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