mlsanityData to 7 Oct 2026

Environmental Sound Extraction Using Onomatopoeic Words

arXiv 2112.00209 ↗First posted 1 Dec 2021cs.SD cs.LG eess.AScited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 1 Dec 2021.

Builds on

10 of 24 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 763
  3. 2018Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separationcited by 400
  4. 2019Universal Sound Separationcited by 41
  5. 2019Conditioned-U-Net: Introducing a Control Mechanism in the U-Net for Multiple Source Separationscited by 27
  6. 2020Listen to What You Want: Neural Network-based Universal Sound Selectorcited by 27
  7. 2019Audio query-based music source separationcited by 17
  8. 2020Source separation with weakly labelled data: An approach to computational auditory scene analysiscited by 12
  9. 2018End-to-End Sound Source Separation Conditioned On Instrument Labelscited by 5
  10. 2020RWCP-SSD-Onomatopoeia: Onomatopoeic Word Dataset for Environmental Sound Synthesiscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

3 papers.