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A Method for Automatic Whoosh Sound Description

Eugene Cherny, Johan Lilius, Dmitry Mouromtsev, A Method for Automatic Whoosh Sound Description. In: Alberto Torin, Brian Hamilton, Stefan Bilbao, Michael Newton (Eds.), Proceedings of the 20th International Conference on Digital Audio Effects (DAFx-17), Edinburgh, UK, September 5–9, 2017, 459–465, University of Edinburgh, 2017.

Abstract:

Usually, a sound designer achieves artistic goals by editing and processing the pre-recorded sound samples. To assist navigation in the vast amount of sounds, the sound metadata is used: it provides small free-form textual descriptions of the sound file content. One can search through the keywords or phrases in the metadata to find a group of sounds that can be suitable for a task. Unfortunately, the relativity of the sound design terms complicate the search, making the search process tedious, prone to errors and by no means supportive of the creative flow. Another way to approach the sound search problem is to use sound analysis. In this paper we present a simple method for analyzing the temporal evolution of the ``whoosh'' sound, based on the per-band piecewise linear function approximation of the sound envelope signal. The method uses spectral centroid and fuzzy membership functions to estimate a degree to which the sound energy moves upwards or downwards in the frequency domain along the audio file. We evaluated the method on a generated dataset, consisting of white noise recordings processed with different variations of modulated bandpass filters. The method was able to correctly identify the centroid movement directions in 77% sounds from a synthetic dataset.

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BibTeX entry:

@INPROCEEDINGS{inpChLiMo17a,
  title = {A Method for Automatic Whoosh Sound Description},
  booktitle = {Proceedings of the 20th International Conference on Digital Audio Effects (DAFx-17), Edinburgh, UK, September 5–9, 2017},
  author = {Cherny, Eugene and Lilius, Johan and Mouromtsev, Dmitry},
  editor = {Torin, Alberto and Hamilton, Brian and Bilbao, Stefan and Newton, Michael},
  publisher = {University of Edinburgh},
  pages = {459–465},
  year = {2017},
  ISSN = {2413-6689},
}

Belongs to TUCS Research Unit(s): Embedded Systems Laboratory (ESLAB)

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