Improved phase vocoder time-scale modification of audio

Laroche, Jean; Dolson, Mark

doi:10.1109/89.759041

Cited by 194 publications

(194 citation statements)

References 12 publications

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“…This process is known as phase propagation. The phase propagation in the new TSM method is based on a modification to the phase-locked vocoder by Laroche and Dolson [21]. The phase propagation in the phase-locked vocoder can be described as follows.…”

Section: Proposed Phase Propagationmentioning

confidence: 99%

“…Thus, they are most suitable for processing of signals which can be represented as a sum of slowly varying sinusoids. Even with these kind of signals however, the phase vocoder TSM introduces an artifact typically described as "phasiness" to the processed sound [21,22]. Furthermore, transients processed with the standard phase vocoder suffer from a softening of the perceived attack, often referred to as "transient smearing" [2,3,23].…”

Section: Introductionmentioning

confidence: 99%

“…Standard phase vocoder TSM techniques [20,21] are based on a sinusoidal model of the input signal. Thus, they are most suitable for processing of signals which can be represented as a sum of slowly varying sinusoids.…”

Section: Introductionmentioning

confidence: 99%

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Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Damskägg

Välimäki

2017

Applied Sciences

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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.

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Section: Proposed Phase Propagationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Damskägg

Välimäki

2017

Applied Sciences

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“…Since it depends on the arbitrary alignment of the time frame, phase is not stable even for a signal with stationary spectral content, and is thus not amenable to direct quantization. The phase vocoder [9,10] takes advantage of the relative unimportance of absolute phase and the fact that the frequency of a sinusoidal component gives the rate of phase change between adjacent frames to extract a stable representation as the phase derivative along time, or instantaneous fre- …”

Section: Phase-vocoder Representationmentioning

confidence: 99%

“…Resynthesis consists of reading the magnitudes and IFs from a sequence of codewords, then integrating the phase-derivatives within each frequency channel to reconstruct a smooth, consistent phase function suitable for OLA resynthesis. To reduce "phasiness" [10], we also quantize and store across-frequency phase differences, and move cumulated phases towards these on reconstruction.…”

Section: Quency (If)mentioning

confidence: 99%

Model-Based Monaural Source Separation Using a Vector-Quantized Phase-Vocoder Representation

Ellis

Weiss

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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A vector quantizer (VQ) trained on short-time frames of a particular source can form an accurate non-parametric model of that source. This principle has been used in several previous source separation and enhancement schemes as a basis for filtering the original mixture. In this paper, we propose the "projection" of a corrupted target signal onto the constrained space represented by the model as a viable model for source separation. We investigate some parameters of VQ encoding, including a more perceptuallymotivated distance measure, and an encoding of phase derivatives that supports reconstruction directly from quantizer output alone. For the problem of separating speech from noise, we highlight some problems with this approach, including the need for sequential constraints (which we introduce with a simple hidden Markov model), and choices for choosing the best quantization for overlapping sources.

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Linear Time‐Frequency Analysis I: Fourier‐Type Representations

Gribonval¹

2008

Time‐Frequency Analysis

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Improved phase vocoder time-scale modification of audio

Cited by 194 publications

References 12 publications

Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Audio Time Stretching Using Fuzzy Classification of Spectral Bins

Model-Based Monaural Source Separation Using a Vector-Quantized Phase-Vocoder Representation

Linear Time‐Frequency Analysis I: Fourier‐Type Representations

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