Learning the higher-order structure of a natural sound
Learning the higher-order structure of a natural sound
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DOI：
10.1088/0954-898x/7/2/005
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发表时间：
1996-05-01
影响因子：
7.8
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
--
作者：
Bell, AJ;Sejnowski, TJ
只关注二阶统计量的无监督学习算法忽略了信号的相位结构(高阶统计量)，它包含了我们所认为的所有信息性的时间和空间重合。在这里，我们讨论如何使用独立分量分析(ICA)算法来阐明自然信号的高阶结构，从而产生其独立的基函数。这一点以指甲敲击牙齿的声音的ICA变换为例进行了说明。由此产生的独立基函数看起来就像声音本身，具有相似的时间包络和相同的音乐音调。因此，它们既反映了数据中固有的相位信息，也反映了频率信息。
Unsupervised learning algorithms paying attention only to second-order statistics ignore the phase structure (higher-order statistics) of signals, which contains all the informative temporal and spatial coincidences which we think of as 'features'. Here we discuss how an Independent Component Analysis (ICA) algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions. This is illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth. The resulting independent basis functions look like the sounds themselves, having similar temporal envelopes and the same musical pitches. Thus they reflect both the phase and frequency information inherent in the data.