Abstract
One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.
Abstract (translated)
声场分类的最大挑战之一是寻找合适的特征来更好地表现和表征环境声音。环境声音通常涉及更多的声源，而在时间谱表示中显示较少的结构。然而，声学场景的背景在声学特性上表现出时间上的均匀性，这表明它可以通过分布统计而不是时间细节来表征。在这项工作中，我们使用听觉总结统计作为ASC任务的特征进行了调查。这一灵感来自最近的一项神经科学研究，该研究表明人类听觉系统倾向于通过时间平均统计来感知声音纹理。在此基础上，我们进一步提出在保留鉴别信息的同时，利用线性判别分析来消除这些统计数据之间的冗余，为声学场景提供一种极端的一致性表示。实验结果表明，与传统的手工特征相比，该特征具有突出的性能。
URL
https://arxiv.org/abs/1904.05243
