A Method Based on ICA and SVM/GMM for Mixed Acoustic Objects Recognition

Authors:
Yaobo Li;Zhiliang Ren;Gong Chen;Changcun Sun
Affiliations:
Dept. of Weaponry Eng., Naval Univ. of Engineering, Wuhan 430033, China;Dept. of Weaponry Eng., Naval Univ. of Engineering, Wuhan 430033, China;Dept. of Electronic Information Eng. Institute Communications Engineering of PLA University, Nanjing 210007, China;Dept. of Weaponry Eng., Naval Univ. of Engineering, Wuhan 430033, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

With independent component analysis (ICA) to realize the blind separation from mixed acoustic objects, a recognition method based on support vector machine/Gaussian mixture models (SVM/GMM) is proposed through extracting linear prediction coefficient (LPC) feature. It is revealed that LPC is consistently better than wavelet energy feature, ICA is efficient algorithm to estimate the unknown signal level. This method uses the output of GMM to adjust the probabilistic output of SVM. The validity of the ICA and SVM/GMM model is verified via examples in mixed acoustic objects recognition system.