F0-estimation-based primary ambient extraction for stereo signals
F0-estimation-based primary ambient extraction for stereo signals
Primary-ambient extraction (PAE) plays an increasingly important role in spatial audio reproduction to achieve an immersive listening experience. The existing PAE algorithms produce notable extraction errors, especially when the primary components are relatively small in magnitude as compared to the ambient components. In this paper, an F0-estimation-based PAE method is proposed. This method explores harmonic structures of the primary components to tap the full potential and utilize the sparsity constraint. The experiment results validate that the F0-estimation-based PAE method achieves 5 dB lower extraction errors than the principal component analysis (PCA) method and the ambient phase estimation with a sparsity constraint (APES) method.
Zhu, Hanxin
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Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Wang, Yue
40d832b2-d9c7-4f8c-93e3-56fca7275658
Zhu, Hanxin
8a052462-84bf-4f6b-953f-f8b5e941e13f
Shi, Chuang
c46f72bd-54c7-45ee-ac5d-285691fccf81
Wang, Yue
40d832b2-d9c7-4f8c-93e3-56fca7275658
Zhu, Hanxin, Shi, Chuang and Wang, Yue
(2021)
F0-estimation-based primary ambient extraction for stereo signals.
In 2021 29th European Signal Processing Conference (EUSIPCO).
IEEE.
5 pp
.
(doi:10.23919/EUSIPCO54536.2021.9616040).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Primary-ambient extraction (PAE) plays an increasingly important role in spatial audio reproduction to achieve an immersive listening experience. The existing PAE algorithms produce notable extraction errors, especially when the primary components are relatively small in magnitude as compared to the ambient components. In this paper, an F0-estimation-based PAE method is proposed. This method explores harmonic structures of the primary components to tap the full potential and utilize the sparsity constraint. The experiment results validate that the F0-estimation-based PAE method achieves 5 dB lower extraction errors than the principal component analysis (PCA) method and the ambient phase estimation with a sparsity constraint (APES) method.
Text
EUSIPCO29_PAE_Submission
- Accepted Manuscript
More information
e-pub ahead of print date: 8 December 2021
Venue - Dates:
29th European Signal Processing Conference, EUSIPCO 2021, , Dublin, Ireland, 2021-08-23 - 2021-08-27
Identifiers
Local EPrints ID: 483662
URI: http://eprints.soton.ac.uk/id/eprint/483662
PURE UUID: 09c1e964-6602-4e43-8456-88a30773d233
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Date deposited: 03 Nov 2023 17:34
Last modified: 18 Mar 2024 04:13
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Contributors
Author:
Hanxin Zhu
Author:
Chuang Shi
Author:
Yue Wang
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