Abstract
On-line frequency-domain blind separation of audio sources performed through Independent Vector Analysis (IVA) suffers from the problem of determining the order of the separated outputs. In this work, we apply a supervised IVA based on pilot components obtained using a bank of Relative Transfer Functions (RTF). The bank is assumed to be available for potential positions of a target speaker within a confined area. In every frame, the most suitable RTF is selected from the bank based on a criterion. The pilot components are obtained as pre-separated target and interference, respectively, through the Minimum-Power Distortionless Beamforming and Null Beamforming. The supervised IVA is tested in a real-world scenario with various levels of up-to-dateness of the bank. We show that the global permutation problem is resolved even when the bank contains only pure delay filters. The Signal-to-Interference Ratio in separated signals is mostly better than that achieved by the pre-separation, unless the bank contains very precise RTFs.
This paper was supported by The Czech Science Foundation through Project No. 17-00902S and partly supported by the Student Grant Scheme 2018 project of the Technical University in Liberec and by the United States Department of the Navy, Office of Naval Research Global, through Project No. N62909-18-1-2040.
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Acknowledgements
We are due to Dr. Francesco Nesta from Synaptics for his helpful comments and useful suggestions.
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Čmejla, J., Kounovský, T., Málek, J., Koldovský, Z. (2018). Independent Vector Analysis Exploiting Pre-learned Banks of Relative Transfer Functions for Assumed Target’s Positions. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_26
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