PCA-ICA Based Acoustic Ambient Extraction

Authors

  • G. Rajitha  M.Tech (Embedded Systems), Department of ECE, SVCE, Tirupati, Andhra Pradesh, India
  • K. Upendra Raju  Assistant Professor, Department of ECE, SVCE, Tirupati, Andhra Pradesh, India

Keywords:

Primary Ambient Extraction (PAE), Ambient Phase, Spatial Audio, Sparsity, Principal Component analysis (PCA), Independent Component Analysis (ICA), Frequency Domain.

Abstract

Primary-ambient extraction (PAE) has been playing an important role in spatial audio analysis-synthesis. Based on the spatial features, PAE decomposes a signal into primary and ambient components, which are then rendered separately. PAE is performed in sub band domain for complex input signals having multiple point-like sound sources. However, the performance of PAE approaches and their key influences for such signals have not been well-studied so far. In this paper, we conducted a study on frequency-domain PAE using principal component analysis (PCA) and independent component analysis (ICA) in the case of multiple sources. We found that the partitioning of the frequency bins is very critical in PAE. Simulation results reveal that the proposed top-down adaptive partitioning method achieves superior performance as compared to the conventional partitioning methods.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-12-31

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
G. Rajitha, K. Upendra Raju, " PCA-ICA Based Acoustic Ambient Extraction , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.51-59, January-February-2018.