论文标题
带有广义摩尔斯小波的散射变换网络及其在音乐流派分类中的应用
The Scattering Transform Network with Generalized Morse Wavelets and Its Application to Music Genre Classification
论文作者
论文摘要
我们建议在散射转换网络（STN）中使用广义的摩尔斯小波（GMW），而不是常用的莫雷特（或Gabor）小波，我们称为GMW-STN，用于信号分类问题。 GMWS形成了真正分析小波的参数化家族，而Morlet小波仅近似分析。 STN中潜在小波过滤器的分析性对于非组织振荡信号（例如音乐信号）尤为重要，因为它通过提供多尺度振幅和相位（以及导致输入信号的频率）信息来提高STN表示的可解释性。我们使用所谓的GTZAN数据库证明了GMW-STN比音乐类型分类中常规STN的优越性。此外，我们通过将其层数增加到典型的两层STN的三层层来展示GMW-STN的性能提高。}
We propose to use the Generalized Morse Wavelets (GMWs) instead of commonly-used Morlet (or Gabor) wavelets in the Scattering Transform Network (STN), which we call the GMW-STN, for signal classification problems. The GMWs form a parameterized family of truly analytic wavelets while the Morlet wavelets are only approximately analytic. The analyticity of underlying wavelet filters in the STN is particularly important for nonstationary oscillatory signals such as music signals because it improves interpretability of the STN representations by providing multiscale amplitude and phase (and consequently frequency) information of input signals. We demonstrate the superiority of the GMW-STN over the conventional STN in music genre classification using the so-called GTZAN database. Moreover, we show the performance improvement of the GMW-STN by increasing its number of layers to three over the typical two-layer STN.}
