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Semi-supervised minimum redundancy maximum relevance feature selection for audio classification

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Abstract

It is still a changing problem of choosing the most relevant ones from multiple features for their specific machine learning tasks. However, feature selection provides an effective solution to it, which aims to choose the most relevant and least redundant features for data analysis. In this paper, we present a feature selection algorithm termed as semi-supervised minimum redundancy maximum relevance. The relevance is measured by a semi-supervised filter score named constraint compensated Laplacian score, which takes advantage of the local geometrical structures of unlabeled data and constraint information from labeled data. The redundancy is measured by a semi-supervised Gaussian mixture model-based Bhattacharyya distance. The optimal feature subset is selected by maximizing feature relevance and minimizing feature redundancy simultaneously. We apply our algorithm in audio classification task and compare it with other known feature selection methods. Experimental results further prove that our algorithm can lead to promising improvements.

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References

  1. Bartsch MA, Wakefield GH (2005) Audio thumbnailing of popular music using chroma-based representations. IEEE Trans Multimedia 7(1):96–104

    Article  Google Scholar 

  2. Benabdeslem K, Hindawi M (2014) Efficient semi-supervised feature selection: constraint, relevance and redundancy. IEEE Trans Knowl Data Eng 26(5):1131–1143

    Article  Google Scholar 

  3. Bhalerao A, Rajpoot N (2003) Selecting discriminant subbands for texture classification. in Proc. BMVC, Norwich, September 2003

  4. Breiman L, Friedman JH, Olshen RA, Charles J (1984) Classification and regression trees. Wadsworth & Brooks, Pacific Grove

    MATH  Google Scholar 

  5. Chao YH, Wang HM, Chang RC (2005) GMM-based Bhattacharyya kernel fisher discriminant analysis for speaker recognition. in Proc. ICASSP, p 649–652

  6. Choi E, Lee C (2003) Feature extraction based on the Bhattacharyya distance. Pattern Recogn 36(8):1703–1709

    Article  Google Scholar 

  7. Chung FRK (1997) Spectral Graph Theory. AMS

  8. Ding C, Peng HC (2003) Minimum redundancy feature selection from microarray gene expression data. in Proc. IEEE CSB, p 523–528

  9. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889

    MathSciNet  MATH  Google Scholar 

  10. Dy JG, Brodley CE, Kak AC, Broderick LS, Aisen AM (2003) Unsupervised feature selection applied to content-based retrieval of lung images. IEEE Trans Pattern Anal Mach Intell 25:373–378

    Article  Google Scholar 

  11. Efron B, Hastie T, Johnstone I, Tibshirani R (2004a) Least angle regression. Ann Stat 25:407–449

    MathSciNet  MATH  Google Scholar 

  12. Efron B, Hastie T, Johnstone I, Tibshirani R (2004b) Least angle regression. Annals of Stastics 32:407–449

    Article  MathSciNet  MATH  Google Scholar 

  13. Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic Press, San Diego

    MATH  Google Scholar 

  14. Geiger JT, Schuller B, Rigoll G (2013) Large-scale audio feature extraction and SVM for acoustic scene classification. in Proc. Applications of Signal Processing to Audio and Acoustics, New Paltz, p 1–4

  15. Giannakopoulos T, Pikrakis A (2014) Introduction to Audio Analysis: A MATLAB Approach, Elsevier Academic Press

  16. Giannakopoulos T, Pikrakis A, Theodoridis S (2008) Gunshot detection in audio streams from movies by means of dynamic programming and Bayesian networks. in Proc. ICASSP

  17. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    MATH  Google Scholar 

  18. He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. in proc. NIPS, Vancouver

  19. Janett WW, Li Y (2009) Estimation of mutual information: A survey. in Proc. Rough Sets and Knowledge Technology, 2009, Gold Coast, Australia, July, 2009

  20. Kailath T (1967) The divergence and Bhattacharyya distance measuresin signal selection. IEEE Trans Commun Technol 15(1):52–60

    Article  Google Scholar 

  21. Misra H, Ikbal S, Bourlard H, Hermansky H (2004) Spectral entropy based feature for robust ASR. in Proc. ICASSP

  22. Panagiotakis C, Tziritas G (2005) A speech/music discriminator based on rms and zero-crossings. IEEE Trans. on Multimedia 7(1):155–166

    Article  Google Scholar 

  23. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  24. Quinlan JR (1993) C4.5:programs for machine learning. Moran Kaufmann Publishers Inc, San Francisco

    Google Scholar 

  25. Ramalingam T, Dhanalakshmi P (2014) Speech/music classification using wavelet based feature extraction Techiques. J Comput Sci 10(1):34–44

    Article  Google Scholar 

  26. Reyes-Aldasoro CC, Bhalerao A (2006) The Bhattacharyya space for feature selection and its application to texture segmentation. Pattern Recogn 39(5):812–826

    Article  MATH  Google Scholar 

  27. Robnik-Šikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23–69

    Article  MATH  Google Scholar 

  28. Ross BC (2014, Feb.) Mutual information between discrete and continuous data sets. PLoS ONE [Online] 9(2):e87357 Available: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087357

  29. Scheirer E, Slaney M (1997) Construction and evaluation of a robust multifeature speech/music discriminator. in Proc. ICASSP

  30. Song L, Smola A, Gretton A, Bedo J, Borgwardt K (2012) Feature selection via dependence maximization. J Mach Learn Res 13:1393–1434

    MathSciNet  MATH  Google Scholar 

  31. Suzuki T, Sugiyama M, Tanaka T (2009) Mutual information approximation via maximum likelihood estimation of density ratio. in Proc. IEEE International Symposium on Information Theory

  32. Wang RY (2011) Research on audio classification under complex environment. Ph. D. dissertation, Beijing University of Posts and Telecommunications

  33. Xu Z, Jin R, Lyu MRR, King K (2009) Discriminative semi-supervised feature selection via manifold regularization. Proc. 21st Int’l Joint Conf. Artificial Intelligence (IJCAI)

  34. Yang XK, He L, Qu D, Zhang WQ, Johnson MT (2016) Semi-supervised feature selection for audio classification based on constraint compensated Laplacian score. EURASIP Journal on Audio, Speech, and Music Processing. doi:10.1186/s13636-016-0086-9

    Google Scholar 

  35. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn 5:1205–1224

    MathSciNet  MATH  Google Scholar 

  36. Zhang D, Chen S, Zhou Z (2008) Constraint score: a new filter method for feature selection with pairwise constraints. Pattern Recogn 41(5):1440–1451

    Article  MATH  Google Scholar 

  37. Zhao Z, Liu H (2007a) Semi-supervised feature selection via spectral analysis. in proc. SIAM Int. conf. Data Mining, Tempe, p 641–646

  38. Zhao Z, Liu H (2007b) Spectral Feature Selection for Supervised and Unsupervised Learning. in Proc. 24th international conference on Machine learning (ICML), p 1151–1157

  39. Zhao Z, Liu H (2012) Spectral feature selection for data mining (data mining and knowledge discovery series). Chapman and Hall-CRC, Boca Raton

    Google Scholar 

  40. Zubair S, Yan F, Wang W (2013) Dictionary learning based sparse coefficients for audio classification with max and average pooling. Digital Signal Process 23(5):960–970

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61673395, No. 61403415, No. 61302107, and No. 61403224).

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Authors and Affiliations

  1. Zhengzhou Information Science and Technology Institute, Zhengzhou, China

    Xu -Kui Yang & Dan Qu

  2. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Liang He & Wei-Qiang Zhang

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  1. Xu -Kui Yang
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  2. Liang He
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  3. Dan Qu
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  4. Wei-Qiang Zhang
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Correspondence to Xu -Kui Yang.

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Yang, X.K., He, L., Qu, D. et al. Semi-supervised minimum redundancy maximum relevance feature selection for audio classification. Multimed Tools Appl 77, 713–739 (2018). https://doi.org/10.1007/s11042-016-4287-0

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  • DOI: https://doi.org/10.1007/s11042-016-4287-0

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