HOG and subband power distribution image features for acoustic scene classification

Victor Bisot, S. Essid, G. Richard
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引用次数: 48

Abstract

Acoustic scene classification is a difficult problem mostly due to the high density of events concurrently occurring in audio scenes. In order to capture the occurrences of these events we propose to use the Subband Power Distribution (SPD) as a feature. We extract it by computing the histogram of amplitude values in each frequency band of a spectrogram image. The SPD allows us to model the density of events in each frequency band. Our method is evaluated on a large acoustic scene dataset using support vector machines. We outperform the previous methods when using the SPD in conjunction with the histogram of gradients. To reach further improvement, we also consider the use of an approximation of the earth mover's distance kernel to compare histograms in a more suitable way. Using the so-called Sinkhorn kernel improves the results on most of the feature configurations. Best performances reach a 92.8% F1 score.
基于HOG和子带功率分布图像特征的声场景分类
声场景分类是一个难点问题,主要是由于声场景中同时发生的事件密度很大。为了捕获这些事件的发生,我们建议使用子带功率分布(SPD)作为特征。我们通过计算频谱图图像各频段振幅值的直方图来提取它。SPD允许我们对每个频带内的事件密度进行建模。我们的方法使用支持向量机在大型声学场景数据集上进行了评估。当SPD与梯度直方图结合使用时,我们的性能优于以前的方法。为了进一步改进,我们还考虑使用推土机距离核的近似值以更合适的方式比较直方图。使用所谓的Sinkhorn内核可以改善大多数特性配置的结果。最佳表现达到92.8%的F1分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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