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Study on Adaptive Comb filter for Auditory Scene Analysis

听觉场景分析的自适应梳状滤波器研究

基本信息

批准号：
12650420
负责人：
NISHI Kazuki
金额：
$ 1.66万
依托单位：
The University of Electro-Communications
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12650420/
关键词：
Quasi-periodic Signal comb filter Auditory Scene Analysis Wiener filter kalman filter Stochastic differential equation constant-Q Critical Bands 櫛形フィルタウィーナーフィルタ振幅変動ピッチ変動

项目摘要

In this study, we have developed optimal filters for quasi-periodic signals involving amplitude and pitch fluctuations (e.g., a real speech or instrumental sound) as a fundamental technology for realization of the auditory scene analysis.First, we derived linear optimal filters based on the quasi-periodic signal model. The Wiener filter solution under the steady-state condition leads to the constant-BW (-bandwidth)/constant-Q mixed comb structure in which each pass-band located at harmonic frequencies has a constant bandwidth in lower frequency, constant-Q characteristic in higher frequency, and those mixture appears in the intermediate frequency. It is shown that such filter banks closely imitates that of the human auditory system. Next, we derived a Kalman filter based on the Ito stochastic differential equation involving unknown random fluctuations as well as the given slowly varying fluctuations. It is verified that the steady-state solution coincides with the Wiener filter one.In addition, we have designed digital filters, which have not only a comb structure for extracting each harmonic component but also a notch structure for suppressing interference between harmonics components, based on digitization of the continuous Kalman filter equation. Finally, we have established a filter system for extracting a specific sound from among the mixed sound, and confirmed the validity of utilizing as the front-end processing for speech recognition.

在这项研究中，我们已经开发了最佳滤波器的准周期信号，涉及幅度和音调波动（例如，（真实的语音或乐器声）作为实现听觉场景分析的基础技术。稳态条件下的维纳滤波器解导致恒带宽/恒Q混合梳状结构，其中位于谐波频率处的每个通带在较低频率处具有恒定带宽，在较高频率处具有恒定Q特性，并且这些混合出现在中频。结果表明，这种滤波器组密切模仿人类听觉系统。接下来，我们推导出一个卡尔曼滤波器的伊藤随机微分方程的基础上，涉及未知的随机波动，以及给定的缓慢变化的波动。此外，在连续卡尔曼滤波方程数字化的基础上，设计了数字滤波器，它不仅具有用于提取各次谐波分量的梳状结构，而且具有用于抑制各次谐波分量间干扰的陷波结构。最后，我们建立了一个过滤系统，用于从混合声音中提取特定的声音，并确认了作为语音识别前端处理的有效性。