Multi-channel neural audio decorrelation using generative adversarial networks.

Autor:	Anemüller, Carlotta, Thiergart, Oliver, Habets, Emanuël A. P.
Předmět:	GENERATIVE adversarial networks CONVOLUTIONAL neural networks SPACE perception AUDITORY perception SIGNALS & signaling
Zdroj:	EURASIP Journal on Audio Speech & Music Processing; 11/5/2024, Vol. 2024 Issue 1, p1-14, 14p
Abstrakt:	The degree of correlation between the sounds received by the ears significantly influences the spatial perception of a sound image. Audio signal decorrelation is, therefore, a commonly used tool in various spatial audio rendering applications. In this paper, we propose a multi-channel extension of a previously proposed decorrelation method based on generative adversarial networks. A separate generator network is employed for each output channel. All generator networks are optimized jointly to obtain a multi-channel output signal with the desired properties. The training objective includes a number of individual loss terms to control both the input-output and the inter-channel correlation as well as the quality of the individual output channels. The proposed approach is trained on music signals and evaluated both objectively and through formal listening tests. Thereby, a comparison with two classical signal processing-based multi-channel decorrelators is performed. Additionally, the influence of the number of output channels, the individual loss term weightings, and the employed training data on the proposed method's performance is investigated. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Full text from SpringerLink