Akashi Satoh, Tadanobu Inoue
ITCC 2005
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.
Akashi Satoh, Tadanobu Inoue
ITCC 2005
Noboru Kamijoh, Tadanobu Inoue, et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Alan E. Rosenbluth, David O. Melville, et al.
SPIE Advanced Lithography 2009
Akashi Satoh, Tadanobu Inoue
Integration, the VLSI Journal