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Appl. Sci. 2017, 7(12), 1293; https://doi.org/10.3390/app7121293

Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Acoustics Laboratory, Department of Signal Processing and Acoustics, Aalto University, FI-02150 Espoo, Finland
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Academic Editor: Gino Iannace
Received: 3 November 2017 / Revised: 3 December 2017 / Accepted: 7 December 2017 / Published: 12 December 2017
(This article belongs to the Special Issue Sound and Music Computing)
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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. View Full-Text
Keywords: audio systems; digital signal processing; music; spectral analysis; spectrogram audio systems; digital signal processing; music; spectral analysis; spectrogram
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Damskägg, E.-P.; Välimäki, V. Audio Time Stretching Using Fuzzy Classification of Spectral Bins. Appl. Sci. 2017, 7, 1293.

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