Learning Multiple Sound Source 2D Localization

Autor:	Moing, Guillaume Le, Vinayavekhin, Phongtharin, Inoue, Tadanobu, Vongkulbhisal, Jayakorn, Munawar, Asim, Tachibana, Ryuki, Agravante, Don Joven
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound sources in an enclosed environment by using multiple microphone arrays. To this end, we use an encoding-decoding architecture and propose two improvements on it to accomplish the task. In addition, we also propose two novel localization representations which increase the accuracy. Lastly, new metrics are developed relying on resolution-based multiple source association which enables us to evaluate and compare different localization approaches. We tested our method on both synthetic and real world data. The results show that our method improves upon the previous baseline approach for this problem. Comment: Published in: 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2012.05515 Zobrazit plný text záznamu View this record from Arxiv