Eco-Environmental Sounds Classification with Time-Frequency Features under Noise Conditions
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, we consider the task of ecoenvironmental sounds classification under noise conditions. 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 timefrequency 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 ecoenvironmental sounds under noise conditions. The result shows that time-frequency features and SVM classifier have better noise immunity.
Time-Frequency Features Matching Pursuit Choi-Williams Distribution Eco-Environmental Sounds
Qingqing Yu Ying Li
School of Mathematics and Computer Science Fuzhou University, Fuzhou, P.R.China 350108
国际会议
重庆
英文
48-52
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）