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用调制级联过程建模自然声音

Modeling Natural Sounds with Modulation Cascade Processes
课程网址: http://videolectures.net/mbc07_turner_mns/  
主讲教师: Richard Turner
开课单位: 伦敦大学学院
开课时间: 2008-02-01
课程语种: 英语
中文简介:
听觉场景分析极具挑战性。 一种可能由大脑采用的方法是在有关其统计结构的先验知识上形成有用的声音表示。 例如,具有谐波部分的声音是常见的,因此时频表示是有效的。 目前大多数代表都集中在较短的组件上。 在这里,我们建议在较长时间尺度上的结构表示,如音素和语音句子。 我们将声音分解为流程的产品,每个流程都有自己的特征时间尺度。 该解调级联涉及经典幅度解调,但传统算法未能完全实现该表示。 一个新方法,概率幅度解调,被证明胜过已建立的方法,并且容易扩展到完整解调级联的表示。
课程简介: Auditory scene analysis is extremely challenging. One approach, perhaps that adopted by the brain, is to shape useful representations of sounds on prior knowledge about their statistical structure. For example, sounds with harmonic sections are common and so time-frequency representations are efficient. Most current representations concentrate on the shorter components. Here, we propose representations for structures on longer time-scales, like the phonemes and sentences of speech. We decompose a sound into a product of processes, each with its own characteristic time-scale. This demodulation cascade relates to classical amplitude demodulation, but traditional algorithms fail to realise the representation fully. A new approach, probabilistic amplitude demodulation, is shown to out-perform the established methods, and to easily extend to representation of a full demodulation cascade.
关 键 词: 听觉场景分析; 特征时间尺度; 解调级联
课程来源: 视频讲座网
最后编审: 2019-05-16:cjy
阅读次数: 88