Multi-class acoustic event classification of hydrophone data

Gorkem Cipli, F. Sattar, P. Driessen
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引用次数: 6

Abstract

In this paper, we address the problem of multi-class classification of hydrophone data for acoustic events using low-dimensional features. A new iterative multiclass classification scheme is proposed based on the combination of adaptive MFCC feature set and an improved HMM-GMM classifier. The adaptive window length for MFCC is important since for acoustic sounds in the ocean, the optimum window length may be different unlike the window length of 16 - 32 msec, which is optimum for speech signals. Further, in order to increase the classification performance, we perform the B-spline approximation to the generated Gaussians parameters of the multi model HMM-GMM classifier to enhance the separation of the decision region. Experimental results for the real recorded hydrophone data show that our improved iterative scheme efficiently classifies the acoustic events with high mean accuracy (96%), sensitivity (95%), and specificity (97%).
水听器数据的多类声事件分类
本文研究了利用低维特征对声学事件的水听器数据进行多类分类的问题。提出了一种基于自适应MFCC特征集和改进HMM-GMM分类器相结合的迭代多类分类方案。MFCC的自适应窗口长度很重要,因为对于海洋中的声学信号,最佳窗口长度可能不同,不像16 - 32毫秒的窗口长度,这是语音信号的最佳窗口长度。此外,为了提高分类性能,我们对多模型HMM-GMM分类器生成的高斯参数进行b样条逼近,以增强决策区域的分离性。对实际记录的水听器数据进行了实验,结果表明改进的迭代方法能够有效地对声事件进行分类,具有较高的平均准确率(96%)、灵敏度(95%)和特异性(97%)。
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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