Kohich Matsuda
Papers
1
Total Citations
3
H-Index
1
About
Kohich Matsuda is a researcher whose work centers on acoustic signal processing, with a particular focus on efficient data representation for sound field analysis. His key contributions lie in developing novel compression models for acoustic transfer functions, which are essential for applications such as sound source localization and separation. In his most cited work, "A Fourier series based Data compression model for Acoustic transfer function" (2020, 3 citations), Matsuda introduced a method that leverages Fourier series expansion to compactly represent large sets of transfer functions typically obtained through measurements or geometrical calculations. This approach addresses a critical bottleneck in acoustic array processing, where managing voluminous transfer function data is necessary for accurate spatial audio reconstruction. While his citation count is modest, his work represents a foundational step toward more efficient acoustic modeling, potentially enabling real-time implementations in hearing aids, robotics, and virtual reality. Matsuda's research contributes to the broader goal of making complex acoustic environments computationally tractable, offering a practical pathway for advancing sound field manipulation technologies.
Research Focus
Key Achievements
Top Papers
- 1