Robust minimum statistics project coefficients feature for acoustic environment recognition
Robust minimum statistics project coefficients feature for acoustic environment recognition
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DOI：
10.1109/icassp.2014.6855206
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发表时间：
2014-05
期刊：
影响因子：
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通讯作者：
Shiwen Deng;Jiqing Han;Chaozhu Zhang;Tieran Zheng;Guibin Zheng
中科院分区：
文献类型：
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作者：
Shiwen Deng;Jiqing Han;Chaozhu Zhang;Tieran Zheng;Guibin Zheng
声环境识别具有广泛的应用前景，但对于现实生活中的复杂环境，声环境识别是一个相当困难的问题。本文提出了一种新的特征，命名为最小统计投影系数（MSPC），并打算解决这个问题。该方法从背景声中提取MSPC特征，该特征比前景声更具有鲁棒性，可用于声环境识别。实验结果表明，与传统的声学特征相比，MSPC特征具有优异的性能，特别是在非常复杂的声学环境中。
Acoustic environment recognition has been widely used in many applications, and is a considerable difficult problem for the real-life and complex environment. This paper proposes a novel feature, named minimum statistics project coefficients (MSPC), and intents to solve this problem. The MSPC feature is extracted from the background sound which is more robust than the foreground sound for the task of acoustic environment recognition. Experimental results show the outstanding performance of the MSPC feature compared with the conventional acoustic features, especially in very complex acoustic environments.