Blind audio source separation based on a new system model and the Savitzky-Golay filter

Xu, Pengfei; Jia, Yinjie; Jiang, Mingxin

doi:10.2478/jee-2021-0029

Abstract

Blind source separation (BSS) is a research hotspot in the field of signal processing. This scheme is widely applied to separate a group of source signals from a given set of observations or mixed signals. In the present study, the Savitzky-Golay filter is applied to smooth the mixed signals, adopt a simplified cost function based on the signal to noise ratio (SNR) and obtain the demixing matrix accordingly. To this end, the generalized eigenvalue problem is solved without conventional iterative methods. It is founded that the proposed algorithm has a simple structure and can be easily implemented in diverse problems. The obtained results demonstrate the good performance of the proposed model for separating audio signals in cases with high signal to noise ratios.

References

[1] J. Herault, C. Jutten, and B. Ans, “Détection de grandeurs primitives dans un message composite par une architecture de calcul neuromimetique en apprentissage non supervise”, Proceedings of the 10 th Symposium on Signal and Image Processing, Nice, France, pp. 1017-1022 (1985).
Search in Google Scholar Back to article
[2] P. Xu, Y. Jia, Z. Wang, and M. Jiang, “Underdetermined Blind Source Separation for Sparse Signals Based on the Law of Large Numbers and Minimum Intersection Angle Rule”, Circuits Systems and Signal Processing, 39, 5, pp. 2442-2458 (2020).10.1007/s00034-019-01263-2
Search in Google Scholar Back to article
[3] Y. Jia and P. Xu, “Convolutive Blind Source Separation for Communication Signals Based on the Sliding Z-Transform”, IEEE Access, 8, pp. 41213-41219 (2020).10.1109/ACCESS.2020.2976700
Search in Google Scholar Back to article
[4] P. Xu and Y. Jia, “Blind source separation based on source number estimation and fast-ICA with a novel non-linear function”, Proceedings of the romanian academy series a-mathematics physics technical sciences information science, 21, 2, 93-194 (2020).
Search in Google Scholar Back to article
[5] D. D. Taralunga, I. Gussi, and R. Strungaru, “A new method for fetal electrocardiogram denoising using blind source separation and empirical mode decomposition”, Rev. Roum. Sci. Techn. - lectrotechn. Et nerg., 61, 1, pp. 94-98 (2016).
Search in Google Scholar Back to article
[6] J. Ma and X. Zhang, “Blind Source Separation Algorithm Based on Maximum Signal Noise Ratio”, Proceedings of the 2008 IEEE First International Conference on Intelligent Networks and Intelligent Systems, Wuhan, China, pp. 625-628 (2008).10.1109/ICINIS.2008.109
Search in Google Scholar Back to article
[7] Z. Gu, T. Fan, Q. Lv, J. Chen, D. Ye, J. Huangfu, and L. Ran, “Remote Blind Motion Separation Using a Single-Tone SIMO Doppler Radar Sensor”, IEEE Transactions on Geoscience and Remote Sensing, 57, 1, pp. 462-472 (2019).10.1109/TGRS.2018.2856902
Search in Google Scholar Back to article
[8] Z. Saleem, S. Al-Ghadhban, and T. Y. Al-Naffouri, “On the use of blind source separation for peak detection in spectrum sensing”, Proceedings of IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, pp. 23 - 25 (2012).10.1109/ICCSCE.2012.6487117
Search in Google Scholar Back to article
[9] Z. Gu, J. Wang, F. Shen, K. Xu, D. Ye, and J. Huangfu, “Blind Separation of Doppler Human Gesture Signals Based on Continuous-Wave Radar Sensors”, IEEE Transactions on Instrumentation and Measurement, 68, 7, pp. 2659-2661 (2019).10.1109/TIM.2019.2896364
Search in Google Scholar Back to article
[10] Y. Zhang, X. Shi, and C. H. Chen, “A Gaussian mixture model for underdetermined independent component analysis”, Signal Processing, 86, 7, pp. 1538-1549 (2006).10.1016/j.sigpro.2005.08.009
Search in Google Scholar Back to article
[11] M. Borga, “Learning multidimensional signal processing”, PhD dissertation, Linkoping University, Sweden (1998).
Search in Google Scholar Back to article
[12] A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data by simplified least squares procedures”, Analytical Chemistry, 36, 11, pp. 1627 - 1639 (1964).10.1021/ac60214a047
Search in Google Scholar Back to article
[13] R. W. Schafer, “What is a Savitzky-Golay filter?”, IEEE Signal Processing Magazine, 28, 4, pp. 111-117 (2011).10.1109/MSP.2011.941097
Search in Google Scholar Back to article
[14] D. Acharya, A. Rani, S. Agarwal, and V. Singh, “Application of Adaptive Savitzky-Golay Filter for EEG Signal Processing”, Perspectives on Science, 8, pp. 677-679 (2016).10.1016/j.pisc.2016.06.056
Search in Google Scholar Back to article
[15] J. Chen, P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh, “A Simple Method for Reconstructing a High-Quality NDVI Time-Series Data Set Based on the Savitzky-Golay Filter”, Remote Sensing of Environment, 91, 3, pp. 332-344 (2004).10.1016/j.rse.2004.03.014
Search in Google Scholar Back to article
[16] E. Vincent, R. Gribonval, and C. Fevotte, “Performance measurement in blind audio source separation”, IEEE Transactions on Audio, Speech, and Language Processing, 14, 4, pp. 1462-1469 (2006).10.1109/TSA.2005.858005
Search in Google Scholar Back to article

Blind audio source separation based on a new system model and the Savitzky-Golay filter

Abstract

Paradigm

My account