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Yuki Okamoto

5 accepted papers

2024

Environmental Sound Synthesis from Vocal Imitations and Sound Event Labels

Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi, Ryotaro Nagase, Takahiro Fukumori, Yoichi Yamashita
ICASSP 2024accepted

One way of expressing an environmental sound is using vocal imitations, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice. We can effectively express the features of environmental sounds, such as rhythm and pitch, using vocal imitations, which cannot be ex…

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2023

Visual Onoma-to-Wave: Environmental Sound Synthesis from Visual Onomatopoeias and Sound-Source Images

Hien Ohnaka, Shinnosuke Takamichi, Keisuke Imoto, Yuki Okamoto, Kazuki Fujii, Hiroshi Saruwatari
ICASSP 2023accepted

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., the text representation of sound. From this perspective, onoma-to-wave has been proposed to synthesize environmental s…

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2022

Environmental Sound Extraction Using Onomatopoeic Words

Yuki Okamoto, Shota Horiguchi, Masaaki Yamamoto, Keisuke Imoto, Yohei Kawaguchi
ICASSP 2022accepted

An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre. We propose an environmental-sound-extraction method using onomatopoeic words to specify the target sound to be extracted. B…

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2022

Sound Event Detection Guided by Semantic Contexts of Scenes

Noriyuki Tonami, Keisuke Imoto, Ryotaro Nagase, Yuki Okamoto, Takahiro Fukumori, Yoichi Yamashita
ICASSP 2022accepted

Some studies have revealed that contexts of scenes (e.g., "home," "office," and "cooking") are advantageous for sound event detection (SED). Mobile devices and sensing technologies give useful information on scenes for SED without the use of acoustic signals. However, conventional methods can employ…

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2021

Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events

Noriyuki Tonami, Keisuke Imoto, Yuki Okamoto, Takahiro Fukumori, Yoichi Yamashita
ICASSP 2021accepted

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 type of the events. The conventional SED methods cannot effectively exploit the difference between the two types of event…

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