← Search

Sakiko Mishima

3 accepted papers

2025

Trainingless Adaptation of Pretrained Models for Environmental Sound Classification

ICASSP 2025accepted

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 unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer lea…

Cited by 0Source ↗Scholar ↗
2024

Low-Rank Constrained Multichannel Signal Denoising Considering Channel-Dependent Sensitivity Inspired by Self-Supervised Learning for Optical Fiber Sensing

ICASSP 2024accepted

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 distributed acoustic sensing (DAS). In DAS, observed data, which is comprised of multichannel data, has suffered from severe n…

Cited by 0Source ↗Scholar ↗
2021

Impact of Sound Duration and Inactive Frames on Sound Event Detection Performance

ICASSP 2021accepted

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 event class, e.g., the sound event "fan" has a long duration, whereas the sound event "mouse clicking" is instantaneous.…

Cited by 25Source ↗Scholar ↗