4 papers · 1 filter
Event Classification by Physics-informed Inpainting for Distributed Multichannel Acoustic Sensor with Partially Degraded Channels
Noriyuki Tonami, Wataru Kohno, Yoshiyuki Yajima +4
Distributed multichannel acoustic sensing (DMAS) enables large-scale sound event classification (SEC), but performance drops when many channels are degraded and when sensor layouts…
Trainingless Adaptation of Pretrained Models for Environmental Sound Classification
Noriyuki Tonami, Wataru Kohno, Keisuke Imoto +4
Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or…
Low-rank constrained multichannel signal denoising considering channel-dependent sensitivity inspired by self-supervised learning for optical fiber sensing
Noriyuki Tonami, Wataru Kohno, Sakiko Mishima +3
Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distri…
Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance
Keisuke Imoto, Sakiko Mishima, Yumi Arai +1
In many methods of sound event detection (SED), a segmented time frame is regarded as one data sample to model training. The durations of sound events greatly depend on the sound e…