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Cited by 71Music and Audio ProcessingMusic Technology and Sound StudiesSpeech and Audio Processing

The DOI matches its Crossref record; 38 of the 41 references it cites were found in a registry or an index; none of the 51 sources its claims cite is checked for support yet.

A Review of Time-Scale Modification of Music Signals

Open access
Driedger, Jonathan, Müller, Meinard·2016·Applied Sciences
DOI 10.3390/app6020057
Open this paper in CoChat — free accountOpen PDF

Abstract

Time-scale modification (TSM) is the task of speeding up or slowing down an audio signal’s playback speed without changing its pitch. In digital music production, TSM has become an indispensable tool, which is nowadays integrated in a wide range of music production software. Music signals are diverse—they comprise harmonic, percussive, and transient components, among others. Because of this wide range of acoustic and musical characteristics, there is no single TSM method that can cope with all kinds of audio signals equally well. Our main objective is to foster a better understanding of the capabilities and limitations of TSM procedures. To this end, we review fundamental TSM methods, discuss typical challenges, and indicate potential solutions that combine different strategies. In particular, we discuss a fusion approach that involves recent techniques for harmonic-percussive separation along with time-domain and frequency-domain TSM procedures.

Overview

CoChat Summary

TL;DR

This review explains time-scale modification (TSM) for music, covering OLA, WSOLA, PV-TSM, and a fusion approach using harmonic-percussive separation. It argues no single TSM method handles all music signals well, and that combining methods can reduce artifacts.

Key findings

  • No single TSM method copes equally well with all audio signals because music contains harmonic, percussive, and transient components with contrasting requirements.
  • OLA is suited to purely percussive signals but causes phase jump artifacts and warbling on harmonic signals; WSOLA preserves the most prominent periodicity but can produce transient doubling/skipping and still warbles on polyphonic inputs.
  • PV-TSM achieves horizontal phase coherence but often smears transients and introduces phasiness due to loss of vertical phase coherence.
  • A fusion approach using harmonic-percussive separation applies PV-TSM with identity phase locking to the harmonic component and OLA with a very short frame length to the percussive component; in the shown example with stretch factor α = 1.8, it preserves a transient without explicit transient detection.

Methods

  • Review/tutorial article, not a new empirical study; it reviews fundamental TSM procedures and discusses typical challenges and potential solutions.
  • Covers OLA, WSOLA, PV-TSM, and TSM based on harmonic-percussive separation, with illustrative examples such as violin plus castanets, Beethoven’s Symphony No. 5 onset synchronization, and singing-voice spectra.
  • Main analysis is conceptual and algorithmic: derivations of frame-based TSM, artifact explanations, parameter choices, and qualitative comparisons.

Limitations

  • The paper states no formal limitations.
  • Evident from the text: it is a review/tutorial without new experiments, listening tests, or quantitative benchmark; perceptual claims are supported by illustrative figures and prior literature rather than systematic evaluation.
  • Evident from the text: examples often use a single stretch factor such as α = 1.8, so they do not establish general performance across all signals or parameter settings.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.3390/app6020057

Cites

38 of 38 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1 Generalized processing pipeline of Time-scale modification (TSM) procedures.
Figure 1 Generalized processing pipeline of Time-scale modification (TSM) procedures.CC-BY · From the PDF
Figure 2 Typical artifacts that occur when choosing the synthesis frames ym to be equal to the analysis frames xm . The input signal x is stretched by a factor of a = 1.8. The output signal y shows discontinuities (blue oval) and amplitude fluctuations (indicated by blue lines).
Figure 2 Typical artifacts that occur when choosing the synthesis frames ym to be equal to the analysis frames xm . The input signal x is stretched by a factor of a = 1.8. The output signal y shows discontinuities (blue oval) and amplitude fluctuations (indicated by blue lines).CC-BY · From the PDF
Figure 3 The principle of TSM based on overlap-add (OLA). ( a ) Input audio signal x with analysis frame xm . The output signal y is constructed iteratively; ( b ) Application of Hann window function w to the analysis frame xm resulting in the synthesis frame ym ; ( c ) The next analysis frame xm + 1 having a specified distance of H a samples from xm ; ( d ) Overlap-add using the specified synthesis hopsize H s.
Figure 3 The principle of TSM based on overlap-add (OLA). ( a ) Input audio signal x with analysis frame xm . The output signal y is constructed iteratively; ( b ) Application of Hann window function w to the analysis frame xm resulting in the synthesis frame ym ; ( c ) The next analysis frame xm + 1 having a specified distance of H a samples from xm ; ( d ) Overlap-add using the specified synthesis hopsize H s.CC-BY · From the PDF
Figure 4 Illustration of a typical artifact for an audio signal modified with OLA. In this example, the input signal x is stretched by a factor of a = 1.8. The OLA procedure is visualized for two frames indicated by window functions. The periodic structure of x is not preserved in the output signal y .
Figure 4 Illustration of a typical artifact for an audio signal modified with OLA. In this example, the input signal x is stretched by a factor of a = 1.8. The OLA procedure is visualized for two frames indicated by window functions. The periodic structure of x is not preserved in the output signal y .CC-BY · From the PDF
Figure 5 The principle of Waveform Similarity Overlap-Add (WSOLA). ( a ) Input audio signal x with the adjusted analysis frame x ′ m . The frame was already windowed and copied to the output signal y ; ( b,c ) Retrieval of a frame from the extended frame region x + m + 1 (solid blue box) that is as similar as possible to the natural progression ˜ xm (dashed blue box) of the adjusted analysis frame x ′ m ; ( d ) The adjusted analysis frame x ′ m + 1 is windowed and copied to the output signal y .
Figure 5 The principle of Waveform Similarity Overlap-Add (WSOLA). ( a ) Input audio signal x with the adjusted analysis frame x ′ m . The frame was already windowed and copied to the output signal y ; ( b,c ) Retrieval of a frame from the extended frame region x + m + 1 (solid blue box) that is as similar as possible to the natural progression ˜ xm (dashed blue box) of the adjusted analysis frame x ′ m ; ( d ) The adjusted analysis frame x ′ m + 1 is windowed and copied to the output signal y .CC-BY · From the PDF
Image 6
CC-BY · From the PDF
Image 7
CC-BY · From the PDF
Image 8
CC-BY · From the PDF
Figure 9 The principle of TSM Based on the Phase Vocoder (PV-TSM). ( a ) STFT X (time-frequency bins are visualized by sinusoidals); ( b ) Using the original phase of X ( m + 1, k ) leads to phase jumps when overlapping the frames at the synthesis hopsize H s (blue oval); ( c ) Update of the sinusoids' phases via phase propagation. Phase jumps are reduced (blue oval); ( d ) Signal reconstruction from the modified STFT X Mod .
Figure 9 The principle of TSM Based on the Phase Vocoder (PV-TSM). ( a ) STFT X (time-frequency bins are visualized by sinusoidals); ( b ) Using the original phase of X ( m + 1, k ) leads to phase jumps when overlapping the frames at the synthesis hopsize H s (blue oval); ( c ) Update of the sinusoids' phases via phase propagation. Phase jumps are reduced (blue oval); ( d ) Signal reconstruction from the modified STFT X Mod .CC-BY · From the PDF
Figure 10 Illustration of a smeared transient as a result of applying PV-TSM. The input signal x has been stretched by a factor of a = 1.8 to yield the output signal y .
Figure 10 Illustration of a smeared transient as a result of applying PV-TSM. The input signal x has been stretched by a factor of a = 1.8 to yield the output signal y .CC-BY · From the PDF
Figure 11 Principle of TSM based on harmonic-percussive separation.
Figure 11 Principle of TSM based on harmonic-percussive separation.CC-BY · From the PDF
Figure 12 Transient preservation in TSM based on harmonic-percussive separation (HPS). The input signal x is the same as in Figure 10 and is stretched by a factor of a = 1.8 using TSM based on HPS.
Figure 12 Transient preservation in TSM based on harmonic-percussive separation (HPS). The input signal x is the same as in Figure 10 and is stretched by a factor of a = 1.8 using TSM based on HPS.CC-BY · From the PDF
Figure 13 The HPS procedure presented in [23]. ( a ) Input audio signal x ; ( b ) STFT X of x ; ( c ) Horizontally and vertically enhanced spectrograms ˜ Y h and ˜ Y p; ( d ) Binary masks M h and M p; ( e ) Spectrograms of the harmonic and the percussive component X h and X p; ( f ) Harmonic and percussive components x h and x p.
Figure 13 The HPS procedure presented in [23]. ( a ) Input audio signal x ; ( b ) STFT X of x ; ( c ) Horizontally and vertically enhanced spectrograms ˜ Y h and ˜ Y p; ( d ) Binary masks M h and M p; ( e ) Spectrograms of the harmonic and the percussive component X h and X p; ( f ) Harmonic and percussive components x h and x p.CC-BY · From the PDF
Figure 14 Example of a non-linear time-stretch function t .
Figure 14 Example of a non-linear time-stretch function t .CC-BY · From the PDF
Figure 15 ( a ) Score of the first five measures of Beethoven's Symphony No. 5; ( b ) Waveforms of two performances. Corresponding onset positions are indicated by red arrows; ( c ) Set of anchor points (indicated in red) and inferred time-stretch function t ; ( d ) Onset-synchronized waveforms of the two performances.
Figure 15 ( a ) Score of the first five measures of Beethoven's Symphony No. 5; ( b ) Waveforms of two performances. Corresponding onset positions are indicated by red arrows; ( c ) Set of anchor points (indicated in red) and inferred time-stretch function t ; ( d ) Onset-synchronized waveforms of the two performances.CC-BY · From the PDF
Figure 16 Pitch-shifting via resampling and TSM. ( a ) Spectrogram of an input audio signal; ( b ) Spectrogram of the resampled signal; ( c ) Spectrogram after TSM application.
Figure 16 Pitch-shifting via resampling and TSM. ( a ) Spectrogram of an input audio signal; ( b ) Spectrogram of the resampled signal; ( c ) Spectrogram after TSM application.CC-BY · From the PDF
Figure 17 Frequency spectra for a fixed frame m of different versions of a singing voice recording. The spectral envelopes are marked in red. ( a ) Original spectrum X ( m ) with spectral envelope G m ; ( b ) Pitch-shifted spectrum X Shift ( m ) with spectral envelope G Shift m ; ( c ) Pitch-shifted spectrum X Mod ( m ) with adapted spectral envelope.
Figure 17 Frequency spectra for a fixed frame m of different versions of a singing voice recording. The spectral envelopes are marked in red. ( a ) Original spectrum X ( m ) with spectral envelope G m ; ( b ) Pitch-shifted spectrum X Shift ( m ) with spectral envelope G Shift m ; ( c ) Pitch-shifted spectrum X Mod ( m ) with adapted spectral envelope.CC-BY · From the PDF

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Citations

Cites

Show all 41 references

Research works printed but not linked: 5

  1. Moinet, A., Dutoit, T., and Latour, P. (2013, January 2–5). Audio time-scaling for slow motion sports videos. Proceedings of the International Conference on Digital Audio Effects (DAFx), Maynooth, Ireland.
  2. Kraft, S., Holters, M., von dem Knesebeck, A., and Zölzer, U. (2012, January 17–21). Improved PVSOLA time stretching and pitch shifting for polpyhonic audio. Proceedings of the International Conference on Digital Audio Effects (DAFx), York, UK.
  3. Moinet, A., and Dutoit, T. (2011, January 19–23). PVSOLA: A phase vocoder with synchronized overlapp-add. Proceedings of the International Conference on Digital Audio Effects (DAFx), Paris, France.
  4. Verma, T.S., and Meng, T.H. (1998, January 19–21). Time scale modification using a sines+transients+noise signal model. Proceedings of the Digital Audio Effects Workshop (DAFx98), Barcelona, Spain.
  5. Gkiokas, A., Papavassiliou, V., Katsouros, V., and Carayannis, G. (2012, January 17–21). Deploying nonlinear image filters to spectrograms for harmonic/percussive separation. Proceedings of the International Conference on Digital Audio Effects (DAFx), York, UK.

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Metadata

Authors
Driedger, J., & Müller, M.
Affiliations
International Audio Laboratories Erlangen (DE)
Corresponding author
  • Driedger, Jonathan
  • Müller, Meinard
ORCID
Müller, Meinard
Venue
Applied Sciences
Publisher
MDPI AG
Published
2016-02-18
Volume
6
Issue
2
Pages
57
ISSN
2076-3417 (ISSN-L)
DOI
10.3390/app6020057
OpenAlex
W2278339107
Cited by
71
References
41
Citations by year
2026: 4 · 2025: 7 · 2024: 11 · 2023: 8 · 2022: 12 · 2021: 6 · 2020: 4 · 2019: 5 · 2018: 8 · 2017: 4 · 2016: 1
FWCI
4.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Deutsche Forschungsgemeinschaft (DFG MU 2686/6-1)
Topics
  • Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
  • Social Sciences › Arts and Humanities › Music › Music Technology and Sound Studies
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Keywords
time-scale modification, music signal processing, music production software

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