A source separation evaluation method in object-based spatial audio. Liu, Q., Wang, W., Jackson, P. J. B., & Cox, T. J. In 2015 23rd European Signal Processing Conference (EUSIPCO), pages 1088-1092, Aug, 2015.
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Paper doi abstract bibtex
Paper doi abstract bibtex Representing a complex acoustic scene with audio objects is desirable but challenging in object-based spatial audio production and reproduction, especially when concurrent sound signals are present in the scene. Source separation (SS) provides a potentially useful and enabling tool for audio object extraction. These extracted objects are often remixed to reconstruct a sound field in the reproduction stage. A suitable SS method is expected to produce audio objects that ultimately deliver high quality audio after remix. The performance of these SS algorithms therefore needs to be evaluated in this context. Existing metrics for SS performance evaluation, however, do not take into account the essential sound field reconstruction process. To address this problem, here we propose a new SS evaluation method which employs a remixing strategy similar to the panning law, and provides a framework to incorporate the conventional SS metrics. We have tested our proposed method on real-room recordings processed with four SS methods, including two state-of-the-art blind source separation (BSS) methods and two classic beamforming algorithms. The evaluation results based on three conventional SS metrics are analysed.
@InProceedings{7362551,
author = {Q. Liu and W. Wang and P. J. B. Jackson and T. J. Cox},
booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
title = {A source separation evaluation method in object-based spatial audio},
year = {2015},
pages = {1088-1092},
abstract = {Representing a complex acoustic scene with audio objects is desirable but challenging in object-based spatial audio production and reproduction, especially when concurrent sound signals are present in the scene. Source separation (SS) provides a potentially useful and enabling tool for audio object extraction. These extracted objects are often remixed to reconstruct a sound field in the reproduction stage. A suitable SS method is expected to produce audio objects that ultimately deliver high quality audio after remix. The performance of these SS algorithms therefore needs to be evaluated in this context. Existing metrics for SS performance evaluation, however, do not take into account the essential sound field reconstruction process. To address this problem, here we propose a new SS evaluation method which employs a remixing strategy similar to the panning law, and provides a framework to incorporate the conventional SS metrics. We have tested our proposed method on real-room recordings processed with four SS methods, including two state-of-the-art blind source separation (BSS) methods and two classic beamforming algorithms. The evaluation results based on three conventional SS metrics are analysed.},
keywords = {acoustic signal processing;array signal processing;audio signal processing;blind source separation;signal reconstruction;source separation evaluation method;complex acoustic scene;object-based spatial audio production;concurrent sound signals;audio object extraction;SS algorithms;sound field reconstruction process;remixing strategy;panning law;real-room recordings;blind source separation;BSS methods;SS evaluation method;beamforming algorithms;SS metrics;Microphones;Measurement;Signal processing algorithms;Array signal processing;Arrays;Magnetic heads;Spatial audio;audio objects;blind source separation;beamforming;evaluation},
doi = {10.1109/EUSIPCO.2015.7362551},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570098689.pdf},
}Downloads: 0
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