计算机与数字工程2017，Vol.45Issue(1)：8-14,106,8.DOI:10.3969/j.issn.1672-9722.2017.01.003
噪音环境下基于时-频特征的生态环境声音的分类
Eco-environmental Sounds Classification with Time-frequency Features under Noise Conditions
摘要
Abstract
Eco-environmental sounds depict the sound content of varieties of creatures' survival and activities in the ecological environment at a time interval.Research on eco-environmental sounds is useful in monitoring of the wildlife and their evolution with time.Due to varieties of noises in the ecological environment,the task of eco-environmental sounds classification under noise conditions is considered.Time-frequency representations have the potential to be powerful features for nonstationary signals.Especially,time-frequency domain features can classify sounds with noise where using frequency-domain features (e.g.,MFCCs) fail.Hence,a classification approach using time-frequency features for eco-environmental sounds under noise conditions is presented in this paper.Matching pursuit (MP) algorithm is proposed to extract time-frequency features (MP-based features,for short) of effective signals.Besides statistical features extracted under Choi-Williams distribution (CWD-based features,for short) also perform more effectively than other conventional audio features under noise conditions.Considering the effectiveness of features and robustness of classifier,a classification model using time-frequency features (the combination features of MP based features and CWD-based features) and support vector machine (MP+CWD-SVM for short) is proposed.Experimentally,CWD+MP-SVM is able to achieve a higher classification rate for eco-environmental sounds under noise conditions.The result shows that time-frequency features and SVM classifier have better noise immunity.关键词
时-频特征/匹配追踪/Choi-Williams分布/生态环境声音Key words
time-frequency features/matching pursuit/Choi-Williams distribution/eco-environmental sounds分类
信息技术与安全科学引用本文复制引用
余清清..噪音环境下基于时-频特征的生态环境声音的分类[J].计算机与数字工程,2017,45(1):8-14,106,8.基金项目
福建省中青年教师教育科研项目(编号:JB14112)资助. （编号:JB14112）
