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 accessAbstract
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.
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Integrity signals
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Figures
![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.](/paper-figures/1e8ef37a307b42f6b6efc4e3cecdba68/a-review-of-time-scale-modification-of-music-signals-figure-13.png)
Verification
Claims the paper makes
0 Valid · 6 Not valid · 28 Not checked · 51 Own results and interpretations
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In the paper
“The mapping between the note onsets of the two performances is therefore non-linear.”
§ A Review of Time-Scale Modification of Music Signals † > 6.2. Harmonic-Percussive Separation
plainstates an association- Asserted: passes — The authors put it forward as true.
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In the paper
“However, the vertical phase coherence, i.e., the phase relationships of sinusoidal components within one frame, is usually destroyed in the phase propagation process.”
§ A Review of Time-Scale Modification of Music Signals † > 5.5. Artifacts
plainstates a cause- Asserted: passes — The authors put it forward as true.
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In the paper
“A prominent problem with WSOLA-like methods is known as transient doubling or stuttering.”
§ A Review of Time-Scale Modification of Music Signals † > 4.2. Artifacts
boostedstates a cause- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“When applied to harmonic signals, the output signals of OLA have a characteristic warbling sound, which is a kind of periodic frequency modulation [6].”
§ A Review of Time-Scale Modification of Music Signals † > 3.2. Artifacts
What the sources say
- Not checked yetcites [6] Laroche, 1999, Improved phase vocoder time-scale modification of audio, IEEE Trans. Speech Audio Process. source
plainstates a cause- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“OLA is usually not suited to modify music.”
§ A Review of Time-Scale Modification of Music Signals † > 3.2. Artifacts
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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47 more claims
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“This task becomes especially challenging with large stretching factors, where conventional methods, such as the phase vocoder, often introduce perceptual artifacts, e.g., transient smearing, loss of presence, and phasiness [3], [4], [10].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [3]
In the citing paper
“The best performing TSM methods apply the Short-Time Fourier Transform (STFT), manipulate the spectrogram of the signal to change its duration, and then apply the inverse STFT to reconstruct the time-scaled signal [3], [4], [12].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [3]
In the citing paper
“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].”
From Audio Time Stretching Using Fuzzy Classification of Spectral Bins (2017) · cites this paper as [3]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Review of Time-Scale Modification of Music Signals † > 3.1. The Procedure
“The Hann window has the nice property that ∑ n∈Z w r -n N 2 = 1 ,(4)”
§ A Review of Time-Scale Modification of Music Signals † > 3.1. The Procedure
“w(r) = 0.5 1 -cos 2π(r+N/2) N-1 , if r ∈ [-N/2 : N/2 -1], 0, otherwise.(3)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ A Review of Time-Scale Modification of Music Signals † > 7.3. Websources
“PV-TSM implemented in Python is included in LibROSA [46].”
§ A Review of Time-Scale Modification of Music Signals † > References
“46. Ellis, D.P.W. A Phase Vocoder in Matlab. Available online: http://www.ee.columbia.edu/dpwe/resources/ matlab/pvoc/ (accessed on 3 December 2015).”
Citations
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Cited by
Lung Sound Classification Using Co-Tuning and Stochastic Normalization
Nguyen, T. T. K., & Pernkopf, F. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3156293 · Cited by 192
Investigation of Data Augmentation Techniques for Disordered Speech Recognition
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Roy, A., & Satija, U. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3292953 · Cited by 57
A comparison of data augmentation methods in voice pathology detection
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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)
- 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
- gold
- doi.org
- 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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