Audio time stretching using fuzzy classification of spectral bins

Eero Pekka Damskägg, Vesa Välimäki*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
139 Downloads (Pure)

Abstract

A novel method for audio time stretching has been developed. In time stretching, the audio signal's duration is expanded, whereas its frequency content remains unchanged. The proposed time stretching method employs the new concept of fuzzy classification of time-frequency points, or bins, in the spectrogram of the signal. Each time-frequency bin is assigned, using a continuous membership function, to three signal classes: tonalness, noisiness, and transientness. The method does not require the signal to be explicitly decomposed into different components, but instead, the computing of phase propagation, which is required for time stretching, is handled differently in each time-frequency point according to the fuzzy membership values. The new method is compared with three previous time-stretching methods by means of a listening test. The test results show that the proposed method yields slightly better sound quality for large stretching factors as compared to a state-of-the-art algorithm, and practically the same quality as a commercial algorithm. The sound quality of all tested methods is dependent on the audio signal type. According to this study, the proposed method performs well on music signals consisting of mixed tonal, noisy, and transient components, such as singing, techno music, and a jazz recording containing vocals. It performs less well on music containing only noisy and transient sounds, such as a drum solo. The proposed method is applicable to the high-quality time stretching of a wide variety of music signals.

Original languageEnglish
Article number1293
JournalApplied Sciences (Switzerland)
Volume7
Issue number12
DOIs
Publication statusPublished - 12 Dec 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Audio systems
  • Digital signal processing
  • Music
  • Spectral analysis
  • Spectrogram

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