good afternoon everyone this video is for explaining our research and i'm the first
author of this research, Naoyuki Terashita. And I'm a saxophone player in Tokyo
The background of this research comes from the difficulties in the practice of wind instruments.
Wind instruments such as saxophone can sound too loud and it's actually impossible to practice them
at home and it's also difficult to keep having a practice spaces such as studios outside home
One of the most common approach to solve this problem is to use wind controllers and
to listen to output sounds through headphones so that its output sound does not exceed
the acceptable level of living spaces. And commonly wind controller has two components
one is sensor for detecting breath and the others are sensors for detecting
fingerings and fingering is a combination of fingers that
hold the keys of instruments which determine the note of output sound
And we argue that current finger fingering detection approach of music controller have an
issue of differences in tactile feelings and we argue that it can lead to ineffective practices.
Current fingering detection use the physical buttons or touch senses which are quite different
from keys of actual instrument that has different strokes and different weights for holding keys.
And we purpose a concept that is turning a wind instrument into a music controller.
And when you think about doing this, this is also important to make it easy to change
between the bare instrument and music controller, because it can be main instrument of the players
so we propose to detect fingerings using only two devices one is microphone and another one is
mouthpiece with a built-in loudspeaker. So players can make it turn it to music controller
only by attaching these two devices and get it back to a bare instrument by removing them
so how we can detect fingerings using these two devices?
The idea of a proposal is every fingering corresponds to its unique frequency
characteristics so inversely, the fingering can be estimated from the frequency characteristics
This is an overview of our proposed method. Loudspeaker in the mouthpiece continuously
plays white noise and reflected sound recorded from the microphone is converted into frequency
domain by using short time fourier transfer and the important thing is that sound level
of the white noise is suppressed to below than the acceptable level for living spaces
and we detect fingerings using machine learning classification model from amplitude spectrum.
We conducted an experiment using an actual
saxophone and the important thing about this experiment is the duration of sampling we set
it to very small value in order to satisfy latency requirement that is latency
is the delay between playing and actual sound feedback to the player and this is
not clear that this small sampling duration is sufficient for detecting fingerings of players
Fortunately we found that our approach is and sampling duration is sufficient for classifying
fingerings because the overall misclassification rate was only one percent
by showing this conclusion slide let me finish the presentation
so thank you for paying attention and if you find it interesting please