105 citations · 149 across the 12 of their papers we have counts for
13 papers · 1 filter
Visual onoma-to-wave: environmental sound synthesis from visual onomatopoeias and sound-source images
Hien Ohnaka, Shinnosuke Takamichi, Keisuke Imoto +3
We propose a method for synthesizing environmental sounds from visually represented onomatopoeias and sound sources. An onomatopoeia is a word that imitates a sound structure, i.e.…
How Information on Acoustic Scenes and Sound Events Mutually Benefits Event Detection and Scene Classification Tasks
Keisuke Imoto, Yuka Komatsu, Shunsuke Tsubaki +1
Acoustic scene classification (ASC) and sound event detection (SED) are fundamental tasks in environmental sound analysis, and many methods based on deep learning have been propose…
Acoustic Scene Classification Using Multichannel Observation with Partially Missing Channels
Keisuke Imoto
Sounds recorded with smartphones or IoT devices often have partially unreliable observations caused by clipping, wind noise, and completely missing parts due to microphone failure…
Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events
Noriyuki Tonami, Keisuke Imoto, Yuki Okamoto +2
In conventional sound event detection (SED) models, two types of events, namely, those that are present and those that do not occur in an acoustic scene, are regarded as the same t…
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…
Joint Analysis of Sound Events and Acoustic Scenes Using Multitask Learning
Noriyuki Tonami, Keisuke Imoto, Ryosuke Yamanishi +1
Sound event detection (SED) and acoustic scene classification (ASC) are important research topics in environmental sound analysis. Many research groups have addressed SED and ASC u…