Abstract: Identifying speech and acoustic sounds is beneficial to the developments of some systems like security monitoring, healthcare and modern video-audio conferencing systems, etc. Since many of the acoustic signals have their own specific generation mechanism, there lacks of a systematic methodology on feature extraction for acoustic sounds. Considering the fact that different audio signals have their own specific characteristics, in the paper, the idea of class dependent feature selection and classification is utilized to solve the problem. Some commonly used acoustic features is studied and then a specific feature subset for each kind of sound signals is selected. With the selected class-dependent feature subsets, the support vector machine with radial basis function kernel is used as the classifier. The experiments are conducted on speech and two kinds of acoustic sounds, i.e., cough and the striking of cup-plate. Compared with the conventional feature selection method, class-dependent feature selection utilizes fewer features to realize a better classification.

Key words: acoustic sounds, classification, class-dependent, feature selection, Mel Frequency Cepstrum Coefficients（MFCC）

摘要： 语音和非语音类声音的识别在很多系统的研发中都有非常重要的作用，如安全监控、医疗保健、现代化的视听会议系统等。虽然绝大多数声音信号都有其独特的发音机制，然而要从其中进行特征的提取往往缺乏系统有效的方法。基于不同的音频信号都有其固有的特点，使用类所属特征选择方法来提取音频中的特征，从而进行分类，并用所提出的方法对语音和两种非语音类声音（咳嗽和杯碟破碎的声音）进行了实验仿真，实验结果表明，与常规的特征选择方法相比，提出的方法用更少的特征实现了更好的分类。

关键词: 声音信号, 分类, 类所属, 特征选择, Mel频率倒谱系数（MFCC）