Improved feature extraction for environmental acoustic classification
Journal of the Acoustical Society of America(2017)
摘要
Modern automated acoustic classifiers have been shown to perform remarkably well with human speech recognition and music genre classification. These problems are well defined; there is a deep understanding of the source signal, and the required robustness of the model can be decreased without significantly sacrificing accuracy. Unfortunately, this simplification creates models that are insufficient when tasked with classifying environmental noise, which is inherently more variable and difficult to constrain. To further close the gap between human and computer recognition, we must find feature extraction techniques that address the additional set of complexities involved with environmental noise. In this paper, we will explore sophisticated feature extraction techniques (e.g., convolutional auto-encoders and scattering networks), and discuss their effect when applied to acoustic classification.
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