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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 0SourceScholar
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 0SourceScholar
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 0SourceScholar