婴儿哭声分类的MFCC特征选择

Natlada Meephiw, P. Leesutthipornchai
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引用次数: 1

摘要

婴儿哭闹以引起注意。哭泣有很多原因(例如,注意力、饥饿、需要换湿尿布)。婴儿的哭声似乎太相似了,很难被人类分类。本文将Mel频率倒谱系数(MFCC)用于特征提取过程。对不同数量的mfcc特征进行了研究和比较,以获得适合的分类技术因子。选择简单而知名的分类技术(决策树、朴素贝叶斯和支持向量机)对婴儿哭声进行分类。支持向量机在准确率和F1score方面都有最高的性能指标,分别为70%和71%。这些是由MFCC的11个特征得到的。从实验结果来看,MFCC:11适用于婴儿哭闹声。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MFCC Feature Selection for Infant Cry Classification
The infant calls for attention by crying. The crying comes from many reasons (e.g., attention, hunger, need to change wet diapers). The infant crying sounds seem too similar and difficult to classify by humans. This paper applies Mel Frequency Cepstral Coefficients (MFCC) for the feature extraction process. Various number of MFCC-feature are investigated and compared to obtain a suitable factor for classification techniques. Simple and well-known classification techniques (decision tree, naive Bayes, and support vector machine) are selected to classify the infant cry sounds. The support vector machine has highest performance metrics in both term of accuracy and F1score that are 70% and 71% respectively. Those are obtained from 11 features of MFCC. From the experimental results, MFCC:11 is suitable for infant crying sounds.
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