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