2 citations · 6 across the 5 of their papers we have counts for
7 papers
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…
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…
RWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesis
Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi +3
Environmental sound synthesis is a technique for generating a natural environmental sound. Conventional work on environmental sound synthesis using sound event labels cannot finely…
Sound Event Detection Using Duration Robust Loss Function
Daichi Akiyama, Keisuke Imoto, Noriyuki Tonami +4
Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound even…
Sound Event Detection by Multitask Learning of Sound Events and Scenes with Soft Scene Labels
Keisuke Imoto, Noriyuki Tonami, Yuma Koizumi +3
Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to…
Overview of Tasks and Investigation of Subjective Evaluation Methods in Environmental Sound Synthesis and Conversion
Yuki Okamoto, Keisuke Imoto, Tatsuya Komatsu +4
Synthesizing and converting environmental sounds have the potential for many applications such as supporting movie and game production, data augmentation for sound event detection…