Auditory processing of reverberation: perceptual and computational investigations

描述（由申请人提供）：人类通常会遇到混响的声音，为了准确地解释声学数据，混响处理必须是人类听觉的基本方面。然而，很少有人知道人类听觉系统如何检测混响，并随后处理混响信号。此外，与健康的人类听众相比，最先进的计算去混响算法表现不佳。听力受损的听众，无论是否植入人工耳蜗，在混响环境中的理解力往往会下降。 本文将通过两种实验方法探讨人类对混响的感知：（1）在真实世界中混响特性的系统测量。这将测量人类经常遇到的混响滤波器属性的分布。(2)使用合成声源或合成混响滤波器进行感知实验。志愿者听众将判断合成声音为“混响”或“干”，并试图匹配来源和过滤器。假设听觉系统必须做出关于源和/或滤波器的假设以在感知上分离两者，并且如果合成源/滤波器违反这些假设，则收听者从混响记录中正确识别它们的能力将降低。 这项工作的更广泛的目标是获得一个基本的了解，如何在声学信号的数量变化，影响人类听众的感知声音和空间感。这项工作将为未来的听力实验、人工耳蜗植入、大型混响空间的公共广播系统和人机界面的自动语音识别系统的设计提供信息。

DESCRIPTION (provided by applicant): Humans commonly encounter reverberant sounds and, in order to accurately interpret acoustic data, reverberation processing must be a fundamental aspect of human audition. However, little is known about how the human auditory system detects reverberation, and subsequently processes reverberant signals. In addition, state-of- the-art computational dereverberation algorithms perform poorly compared with healthy human listeners. Hearing-impaired listeners, with and without cochlear implants, often show decreased comprehension in reverberant environments. Human perception of reverberation will be explored via two experimental methods: (1) Systematic measurement of reverberation properties in real-word spaces. This will measure the distribution of reverberation filter properties that humans regularly encounter. (2) Perceptual experiments with synthetic sources or synthetic reverberation filters. Volunteer listeners will judge synthetic sounds as "reverberant" or "dry" and attempt to match sources and filters. It is hypothesized that the auditory system must make assumptions about the source and/or filter to perceptually separate the two and if the synthetic source/filter violates these assumptions, listener's ability to correctly identify them from reverberant recordings will degrade. The broader objective of this work is to gain a basic understanding of how quantitative changes in an acoustic signal, affect both the perceived sound and sense of space in human listeners. Such work will inform the design of future audition experiments, cochlear implants, public-announcement systems for large reverberant spaces and automated voice-recognition systems for human-computer-interfaces.