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Audio sounds classification using scattering features and support vectors machines for medical surveillance

机译：使用散射特征和支持向量机的音频声音分类，用于医疗监视

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This paper proposes a new approach to recognize environmental sounds for audio surveillance and security applications. The sounds are extremely versatile, including sounds generated in domestic, business, and outdoor environments. Since this variability is hard to model, investigations concentrate mostly on specific classes of sounds. Among those, the system that is able to recognize indoor environmental sounds may be of great importance for surveillance and security applications. These functionalities can also be used in portable teleassistive devices to inform disabled and elderly persons affected in their hearing capabilities about specific environmental sounds (door bells, alarm signals, etc.). We propose to apply an environmental sounds classification method, based on scattering transform and the principal component analysis (PCA). Our method integrates ability of PCA to de-correlate the coefficients by extracting a linear relationship with what of scatter transform analysis to derive feature vectors used for environmental sounds classification. The performance evaluation shows the superiority of this novel sound recognition method. The support vector machines method based on Gaussian kernel is used to classify the datasets due to its capability to deal with high-dimensional data. Our SVM-based multiclass classification approach seems well suited for real-world recognition tasks. Experimental results have revealed the good performance of the proposed system and the classification accuracy is up to 92.22%. (C) 2017 Elsevier Ltd. All rights reserved.
机译：本文提出了一种用于识别音频监视和安全应用环境声音的新方法。声音具有多种用途，包括在家庭，商业和室外环境中产生的声音。由于这种可变性很难建模，因此研究主要集中在特定类别的声音上。其中，能够识别室内环境声音的系统对于监视和安全应用可能非常重要。这些功能也可以用于便携式远程辅助设备中，以向残障人士和受影响听觉能力的老年人告知特定的环境声音（门铃，警报信号等）。我们建议基于散射变换和主成分分析（PCA）应用环境声音分类方法。我们的方法整合了PCA的能力，方法是通过与散射变换分析之间提取线性关系来提取系数的相关性，从而得出用于环境声音分类的特征向量。性能评估显示了这种新颖的声音识别方法的优越性。基于高斯核的支持向量机方法由于能够处理高维数据而被用于分类数据集。我们基于SVM的多类分类方法似乎非常适合现实世界中的识别任务。实验结果表明，该系统性能良好，分类准确率高达92.22％。 （C）2017 Elsevier Ltd.保留所有权利。

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