SALADnet: Self-Attentive multisource Localization in the Ambisonics Domain

Autor:	Grumiaux, Pierre-Amaury, Kitic, Srdan, Srivastava, Prerak, Girin, Laurent, Guérin, Alexandre
Rok vydání:	2021
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	In this work, we propose a novel self-attention based neural network for robust multi-speaker localization from Ambisonics recordings. Starting from a state-of-the-art convolutional recurrent neural network, we investigate the benefit of replacing the recurrent layers by self-attention encoders, inherited from the Transformer architecture. We evaluate these models on synthetic and real-world data, with up to 3 simultaneous speakers. The obtained results indicate that the majority of the proposed architectures either perform on par, or outperform the CRNN baseline, especially in the multisource scenario. Moreover, by avoiding the recurrent layers, the proposed models lend themselves to parallel computing, which is shown to produce considerable savings in execution time. Comment: Accepted to Workshop on Applications of Signal Processing to Audio and Acoustics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.11066 Zobrazit plný text záznamu View this record from Arxiv