| 冲击声的稀疏特征提取及声源类型识别 |
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| 引用本文： | 梁雍,陈克安.冲击声的稀疏特征提取及声源类型识别[J].声学学报,2018,43(4):708-718. |
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| 作者姓名： | 梁雍 陈克安 |
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| 作者单位： | 西北工业大学 航海学院 西安 710072 |
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| 基金项目： | 国家自然科学基金项目(11074202,11574249)资助 |
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| 摘 要： | 针对低信噪比下声源材料类型的细分任务,将稀疏表达用于冲击声信号的声源类型识别,提取的稀疏特征相比传统的MFCC特征有效改善了识别性能。分别基于3种预定义词典和一组根据训练信号学习的词典,利用正交匹配追踪(OMP)方法对录制冲击声进行稀疏表达,提取稀疏特征用于不同信噪比下冲击声信号的声源辨识,并与MFCC特征进行比较。对包含12类材料的冲击声数据库的分类结果显示,在几乎所有情况下,稀疏特征比MFCC特征具有更好的识别效果。特别是在信噪比较低的情况下,稀疏特征具有更好的抗噪性能。
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| 关 键 词： | 冲击声 声源类型识别 稀疏表达 低信噪比 |
| 收稿时间： | 2017-02-15 |
Sparse feature extraction and sound source classification for impact sounds |
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| Affiliation: | Department of Environmental Engineering, School of Marline Science and Technology Northwestern Polytechnical University Xi'an 710072 |
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| Abstract: | The sparse representation is used in source recognition of impact signals, aiming at classification more efficiently and exactly for signals under low SNR environment. It is shown that the sparse features perform better than the traditional audio features, MFCCs. The Orthogonal Matching Pursuit (OMP) is applied on three types of predefined dictionaries as well as a learned dictionary based on training signals respectively to obtain sparse representation of recorded impact sounds. The sparse features are extracted for recognition of the sound sources with different SNRs, and the performances are compared with that of MFCCs. The experimental results on the classification of 12 types of materials show that sparse features perform better than MFCC in almost all cases. Specifically, sparse features have better anti-noise performance under low SNR environment. |
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