KOYAMA, Shoichi
Associate Professor, Digital Content and Media Sciences Research Division
National Institute of Informatics[MAIN BODY]■ Fascination with the study of soundI first got involved in researching sound when I joined Nippon Telegraph and Telephone
Corporation (NTT) after completing my Master’s degree and was assigned to the audio
research team. At the time, we were researching how to transmit and reproduce audio in
remote communication, to recreate the feeling of directly speaking to someone face to face.
This requires technology to accurately measure the spatial information from actual sounds andreproduce it in a realistic way.
This is a fundamental technology, but the further I progressed with my research, the morechallenges and new research topics I found one after another, and I became fascinated by thedepth of the physical phenomenon of sound. I also found it hugely rewarding to see thegrowing potential for applying the findings. I moved from NTT to the University of Tokyo’s
Graduate School of Information Science and Technology, and then to the National Institute ofInformatics (NII), but I continued to research sound, simply because I am fascinated by itsstudy.

■ Establishing a new methodology incorporating physical information
The fundamental technologies that are central to measuring and synthesizing sound include
signal processing and machine learning. When it comes to both signal processing andmachine learning, a large number of technologies and methodologies already exist, but theapplication of existing technologies does not actually work very well.
I wondered whether we could establish a new methodology by incorporating physicalinformation into signal processing and machine learning. In other words, the research topic I
am currently working on is “signal processing and machine learning incorporating wave field
properties for sound space analysis and synthesis”.
There are various techniques for machine learning, depending on the purpose. As one
example, I am researching deep neural networks that use training data to measure wave fields
with a small number of sensors. If you rely on normal machine learning, overfitting tends tooccur. This means that when there is insufficient training data, the model does not perform
well with unknown data. We can prevent overfitting by training the model under physicalconstraints.

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With VR and AR, you can enjoy realistic sounds through a headset, but it may sound slightly
different from what you hear in a real-world environment. This is because each person has adifferent head-related transfer function (HRTF) which characterizes how the ear receives asound. Technology using deep neural networks to easily measure and interpolate a person’s
HRTF is another area of my research.
One topic that I want to explore further is spatial active noise control. Noise-cancelling
earphones are already in practical use, but what I am researching is a technology using
multiple speakers to reduce unwanted noise in a specific space without wearing devices suchas earphones or installing physical sound barriers. Once this technology is established, it
could be applied to situations such as remote working spaces at rail stations.
One thing that all my research has in common is that it incorporates physical information. I
believe this results in a superior ability to theoretically research fundamental technologies,and going forward, I hope to expand my research with this principle.

■ Addressing social issues by controlling soundIn today’s society, thanks to modernization and urbanization, we are surrounded by all kindsof sounds. Because humans have an excellent sense of hearing, artificial sounds cause a great
deal of stress. Surveys have found that traffic noise, even at a barely noticeable level, has anadverse effect on the human body comparable to that of air pollution. By refining fundamental
technologies relating to sound, my goal is to eliminate social issues caused by sound, such as
damage to health, as much as possible.

Sound is a type of wave, just like ultrasonic waves, electromagnetic waves, and light. By
understanding sound in detail, our findings could be applied to other types of wave
phenomena. Since sound uses different sensing devices from other types of waves, it may takesome time before the findings are applied, but I believe various applications will be possible
in future.

Researchmap https://researchmap.jp/shoichi_koyama?lang=en
Lab. https://www.ap.nii.ac.jp/

Written by SATO Naoki, translated by FORTE Science Communications.
Updated December 2023

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