Discriminative Minimum Statistics Projection Coefficient Feature for Acoustic Context Recognition

Abstract:

Acoustic environment recognition, which can provide the important acoustic context, has been widely used in many applications and is a considerable difficult problem in the real-life and the complex environment. This paper proposes the discriminative minimum statistics project coefficient (MSPC) feature with the information of classification by using partial least squares (PLS). With the minimum statistics (MS) tracked from the input sound, the discriminative MSPC feature is extracted by projecting the MS into lower-dimensional feature subspace learned by using PLS analysis. Based on the proposed discriminative MSPC feature, the acoustic environment recognition is implemented by using Gaussian Mixture Model (GMM) for modeling each sound class. The experimental results show that the proposed discriminative MSPC feature based on PLS outperforms the MSPC feature based on PCA for acoustic environment recognition.

• Periodical: Applied Mechanics and Materials (Volume 654)
• Pages: 304-309
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.654.304
• Online since: October 2014
• Authors: Shi Wen Deng, Chao Zhu Zhang, Chao Wang
• Keywords: Acoustic Environment Recognition (AER), Audio Context, Background Sound, Context-Aware, Partial Least Squares Analysis

References

[1] A. K. Dey: Understanding and using context, Personal and ubiquitous computing, vol. 5, no. 1, pp.4-7, (2001).

[2] J. J. Aucouturier, Y. Nonaka, K. Katahira and K. Okanoya: Segmentation of expiratory and inspiratory sounds in baby cry audio recordings using hidden Markov models, J. Acoust. Soc. Amer, vol. 130, no. 5, pp.2969-2977, (2011).

DOI: 10.1121/1.3641377

[3] J. Pineau, M. Montemerlo, M. Pollack, N. Roy and S. Thrun: Towards robotic assistants in nursing homes: Challenges and results, Special Iss. Socially Interactive Robots, Robot., Autonomous Syst., vol. 42, no. 3-4, pp.271-281, (2003).

DOI: 10.1016/s0921-8890(02)00381-0

[4] A. Kalmbach, Y. Girdhar and G. Dudek: Unsupervised Environment Recognition and Modeling using Sound Sensing, in Proc. Robotics and Automation, (2013), pp.2699-2704.

DOI: 10.1109/icra.2013.6630948

[5] S. Chu, S. Narayanan, C. -C. J. Kuo and M. J. Mataric: Where am I? Scene recognition for mobile robots using audio features, in Proc. ICME, (2006), pp.885-888.

DOI: 10.1109/icme.2006.262661

[6] T. Heittola, A. Mesaros, A. Eronen and T. Virtanen: Context-dependent sound event detection, EURASIP Journal on Audio, Speech, and Music Processing, (2013), doi: 10. 1186/1687-4722-2013-1.

DOI: 10.1186/1687-4722-2013-1

[7] R. Cai, L. Lu, A. Hanjalic, H. Zhang and L. -H. Cai: A flexible framework for key audio effects detection and auditory context inference, IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 3, pp.1026-1039, (2006).

DOI: 10.1109/tsa.2005.857575

[8] R. Cai, L. Lu and A. Hanjalic: Co-clustering for auditory scene categorization, IEEE Trans. on Multimedia, vol. 18, no. 6, pp.596-606, (2008).

DOI: 10.1109/tmm.2008.921739

[9] A. J. Eronen, V. T. Peltonen, J. T. Tuomi, A. P. Klapuri, S. Fagerlund, T. Sorsa, G. Lorho and J. Huopaniemi: Audio-Based context recognition, IEEE Trans. on Audio, Speech, and Language Processing, vol. 14, no. 1, pp.321-329, (2006).

DOI: 10.1109/tsa.2005.854103

[10] L. Ma, B. Milner and D. Smith: Acoustic environment classification, ACM Trans. Speech Lang. Process., vol. 3, no. 2, pp.1-22, (2006).

DOI: 10.1145/1149290.1149292

[11] S. Chu, S. Narayanan and C. -C. Jay Kuo: Environmental sound recognition with time-frequency audio features, IEEE Trans. on Audio, Speech, and Language Processing, vol. 17, no. 6, pp.1142-1158, (2009).

DOI: 10.1109/tasl.2009.2017438

[12] R. Mogi and H. Kasai: Noise-robust environmental sound classification method based on combination of ICA and MP features, Artificial Intelligence Research, vol. 2, no. 1, pp.107-121, (2013).

DOI: 10.5430/air.v2n1p107

[13] S. -W Deng, J. -Q Han, C. -Z Zhang, T. -R Zheng and G. -B Zheng: Robust minimum statistics project coefficients feature for acoustic environment recognition, in Proceedings of IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), (2014).

DOI: 10.1109/icassp.2014.6855206

[14] B. V. Srinivasan, Y. -C. Luo, G. -R. Daniel, D. N. Zotkin and R. Duraiswami: A symmetric kernel partial least squares framework for speaker recognition, IEEE Transactions on Audio, Speech and Language Processing, vol. 21, no. 7, pp.1415-1423, (2013).

DOI: 10.1109/tasl.2013.2253096

[15] Q. Wang, F. Chen, Xu W and M. H. Yang: Object tracking via partial least squares analysis, IEEE Transactions on Image Processing, vol. 21, no. 10, pp.4454-4465, (2012).

DOI: 10.1109/tip.2012.2205700

[16] R. Martin: Noise power spectral density estimation based on optimal smoothing and minimum statistics, IEEE Trans. on Speech and Audio Processing, vol. 9, no. 5, pp.504-512, (2001).

DOI: 10.1109/89.928915

[17] A. Hoskuldsson: PLS regression methods, Journal of Chemometrics, vol. 2, pp.211-228, (1988).

[18] Online free sound resource on http: /www. freesound. org.