噪音环境下基于时-频特征的生态环境声音的分类

摘要

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.%生态环境声音描述的是在一个时段里生态环境下各种生物发出的声音.对生态环境声音的研究可以用于监测野生动物随着时间进化的情况.由于生态环境中有各种各样的噪音,因此所研究的项目是基于噪音环境的生态环境声音识别.对于不稳定信号,时频特征是潜在的很有用的特征.尤其是,当使用频域特征(如MFCCs)对带有噪音的声音进行分类失败的时候,时-频域特征能成功进行分类.因此,论文提出了一种用时频特征对噪音环境下的生态环境声音进行分类的方法.匹配追踪(MP)算法用于提取有效信号的时频特征.此外,在噪音环境下,Choi-Williams分布下提取的统计特征比其他的传统音频特征更有效.考虑到特征的有效性和分类器的鲁棒性,该文提出一种基于时频特征和支持向量机的分类模型(简称:MP+CWD-SVM).实验证明,在噪音环境下,对生态环境声音进行分类,MP+CWD-SVM可以达到更高的分类正确率.结果显示时频特征和SVM分类器具有更好的抗噪性.