A study on signal extraction in noisy and reverberant environment

本研究以人类心理声学和听觉生理学的知识为基础，探讨语音增强和分离的模型。消除模型用于增强语音。特别注意通过使用空间滤波技术来降低噪声，并且通过使用频率滤波技术来增加基频估计的鲁棒性。这两种技术都采用了取消模型的概念。此外，Bregman提出的启发式搜索的一些约束条件被用来克服与隔离两个声源相关的问题。仿真结果表明，空域滤波和频域滤波都能有效地增强语音。因此，这些滤波方法可以有效地用于自动语音识别系统的前端，并用于语音特征提取。声音分离模型可以从噪声信号中精确地提取出期望信号，即使在波形中也是如此。此外，本研究还讨论了基于哺乳动物听觉生理数据的声源方向估计模型。该模型可以解释神经放电传递的时间和相位信息与耳间时差精度之间的关系。

This research discusses models of speech enhancement and segregation based on knowledge about human psychoacoustics and auditory physiology. The cancellation model is used for enhancing speech. Special attention is paid to reducing noise by using a spatial filtering technique, and increasing the robustness of fundamental frequency estimation by using a frequency filtering technique. Both techniques adopt concepts of the cancellation model. In addition, some constraints related to the heuristic regularities proposed by Bregman are used to overcome the problem associated with segregating two acoustic sources. Simulated results show that both spatial and frequency filtering are useful in enhancing speech. As a result, these filtering methods can be used effectively at the front-end of automatic speech recognition systems, and for speech feature extraction. The sound segregation model can precisely extract a desired signal from a noisy signal even in waveforms.Additionally, this research discusses models of sound source direction estimation based on physiological data of mammal audition. The model can explain the relationship between transmission of temporal and phase information by nerve firing and accuracy of interaural time differences.