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Onoma-to-wave: Environmental Sound Synthesis from Onomatopoeic Words

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In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words. As one way of expressing an environmental sound, we can use an onomatopoeic word, which is a character sequence for phonetically imitating a sound. An onomatopoeic word is effective for describing diverse sound features. Therefore, the use of onomatopoeic words as input for environmental sound synthesis will enable us to generate diverse sounds. To generate diverse sounds, we propose a method based on a sequence-to-sequence framework for synthesizing environmental sounds from onomatopoeic words. We also propose a method of environmental sound synthesis using onomatopoeic words and sound event labels. The use of sound event labels in addition to onomatopoeic words enables us to capture each sound event’s feature depending on the input sound event label. Our subjective experiments show that our proposed methods achieve higher diversity and naturalness than conventional methods using sound event labels.
Title: Onoma-to-wave: Environmental Sound Synthesis from Onomatopoeic Words
Description:
In this paper, we propose a framework for environmental sound synthesis from onomatopoeic words.
As one way of expressing an environmental sound, we can use an onomatopoeic word, which is a character sequence for phonetically imitating a sound.
An onomatopoeic word is effective for describing diverse sound features.
Therefore, the use of onomatopoeic words as input for environmental sound synthesis will enable us to generate diverse sounds.
To generate diverse sounds, we propose a method based on a sequence-to-sequence framework for synthesizing environmental sounds from onomatopoeic words.
We also propose a method of environmental sound synthesis using onomatopoeic words and sound event labels.
The use of sound event labels in addition to onomatopoeic words enables us to capture each sound event’s feature depending on the input sound event label.
Our subjective experiments show that our proposed methods achieve higher diversity and naturalness than conventional methods using sound event labels.

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