# Environmental Sound Extraction Using Onomatopoeic Words

arXiv: https://arxiv.org/abs/2112.00209 · HTML: https://mlsanity.com/paper/2112.00209/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Universal Sound Separation](https://mlsanity.com/paper/1905.03330/) (2019)
- [Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separations](https://mlsanity.com/paper/1907.01277/) (2019)
- [Listen to What You Want: Neural Network-based Universal Sound Selector](https://mlsanity.com/paper/2006.05712/) (2020)
- [Audio query-based music source separation](https://mlsanity.com/paper/1908.06593/) (2019)
- [Source separation with weakly labelled data: An approach to computational auditory scene analysis](https://mlsanity.com/paper/2002.02065/) (2020)
- [End-to-End Sound Source Separation Conditioned On Instrument Labels](https://mlsanity.com/paper/1811.01850/) (2018)
- [RWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesis](https://mlsanity.com/paper/2007.04719/) (2020)

## Built on by (most cited first, 3 of 3)

- [Real-Time Target Sound Extraction](https://mlsanity.com/paper/2211.02250/) (2022)
- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [Fine-grained Soundscape Control for Augmented Hearing](https://mlsanity.com/paper/2603.00395/) (2026)

## Abstract (arXiv)

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. By this method, we estimate a time-frequency mask from an input mixture spectrogram and an onomatopoeic word using a U-Net architecture, then extract the corresponding target sound by masking the spectrogram. Experimental results indicate that the proposed method can extract only the target sound corresponding to the onomatopoeic word and performs better than conventional methods that use sound-event classes to specify the target sound.