Anemüller C, Thiergart O, Habets E (2022)
Publication Type: Journal article
Publication year: 2022
Book Volume: 29
Pages Range: 2477-2481
The degree of correlation between two audio signals entering the ears is known to have a significant impact on the spatial perception of a sound image. Audio signal decorrelation is therefore a widely used tool in various applications within the field of spatial audio processing. This paper explores for the first time the use of a data-driven approach for audio decorrelation. We propose a convolutional neural network architecture that is trained with the help of a state-of-the-art reference decorrelator. The proposed approach is evaluated using music and applause signals by means of objective evaluations as well as through a listening test. The proposed approach can serve as a proof of concept to address common limitations of existing decorrelation techniques in future work, which include introduction of temporal smearing and coloration artifacts and the production of a limited number of mutually uncorrelated output signals.
APA:
Anemüller, C., Thiergart, O., & Habets, E. (2022). A Data-Driven Approach to Audio Decorrelation. IEEE Signal Processing Letters, 29, 2477-2481. https://dx.doi.org/10.1109/LSP.2022.3224833
MLA:
Anemüller, Carlotta, Oliver Thiergart, and Emanuël Habets. "A Data-Driven Approach to Audio Decorrelation." IEEE Signal Processing Letters 29 (2022): 2477-2481.
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