Bi-ear time delay estimating method based on frequency division and improved generalized cross correlation

Abstract

The invention provides a bi-ear time delay estimating method based on frequency division and improved generalized cross correlation in reverberation environment, and relates to the field of sound source positioning. A Gammatone filter is used to effectively simulate characteristics of a basal membrane of a human ear, voice signals are subjected to frequency division processing, and two-ear cross-correlation delay is estimated under a reverberation environment. Compared with a generalized cross correlation delay estimating method, the method can estimate time delay more accurately. The sound source positioning system has better robustness. A Gammatone filter is used to conduct frequency dividing processing for bi-ear signals, and each sub-band signal is subjected to inverse transformation to a time domain after reverberation processing of cepstrum and pre-filtering. Each sub-band signal of left and right ears are subjected to generalized cross correlation operation, an improved phase transformation weight function is employed in a generalized cross correlation algorithm to obtain cross correlation value of each sub-band for summing operation, and the bi-ear time difference corresponding to maximal cross correlation value is obtained.

Description

technical field [0001] The invention belongs to the field of sound source localization, in particular to a generalized cross-correlation binaural time delay estimation method based on frequency division and improvement. Background technique [0002] With the progress of human society, people have higher and higher requirements for the human-computer interaction performance of machines. What human-computer interaction really needs is better coupling between humans and machines or computers, and comprehensive and intuitive communication and communication, rather than simply better designing the surface characteristics of the interactive interface. Increasing communication between humans and machines requires localization and tracking of sound sources, automatic camera tracking for video and audio applications, microphone array beamforming for noise and reverberation suppression, long-distance speech speech recognition and robotic audio systems are sources of speech An example...

AI Technical Summary

Problems solved by technology

When collecting speech in a closed home environment, it often carries various noises from the surrounding environment, room reverberation, and other sound sources. The existence of these interferences reduces the sound clarity and increases the error of sound source positioning, preventing The system fully extracts any voice features, hindering the widespread use of human-computer voice interaction technology

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