activity
20192022
most citedRWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesis

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SD2022

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.…

cs.SD2021

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…

cs.SD20202 cited

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…

cs.SD20201 cited

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…

cs.SD2019

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…