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首页> 外文会议>International Conference on Management, Manufacturing and Materials Engineering >Normal and Abnormal Non-Speech Audio Event Detection Using MFCC and PR-Based Feature Sets
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Normal and Abnormal Non-Speech Audio Event Detection Using MFCC and PR-Based Feature Sets

机译：使用MFCC和基于PR基功能集的正常和异常的非语音音频事件检测

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摘要

Non-speech audio event detection and classification has become a very active subject of research, since it can be implemented in many important areas: audio surveillance and context awareness systems. In this study, non-speech normal and abnormal audio events were detected by Mel-frequency cepstrum coefficients (MFCC) and Pitch range (PR) based features using artificial neural network (ANN) classifiers. We have 4 abnormal events (glass breaking, dog barking, scream, gunshot) and 2 normal events (engine noise and rain). Event detection, using ANN classifiers, resulted in an accuracy of up to 92%, with recognition rates overall in the range of 78%- 87.5%.
机译：非语音音频事件检测和分类已成为一个非常有效的研究主题，因为它可以在许多重要领域实现：音频监控和背景感知系统。在本研究中，使用人工神经网络（ANN）分类器，通过熔融频率谱系数（MFCC）和间距（PR）特征来检测非语音正常和异常音频事件。我们有4个异常事件（玻璃破碎，狗吠叫，尖叫，枪声）和2个正常事件（发动机噪音和雨）。使用ANN分类器的事件检测导致高达92％的精度，总体识别率在78％ - 87.5％的范围内。

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