The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index; none of the 13 sources its claims cite is checked for support yet.
Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation
Open accessAbstract
A major problem in time-scale modification (TSM) of music signals is that percussive transients are often perceptually degraded. To prevent this degradation, some TSM approaches try to explicitly identify transients in the input signal and to handle them in a special way. However, such approaches are problematic for two reasons. First, errors in the transient detection have an immediate influence on the final TSM result and, second, a perceptual transparent preservation of transients is by far not a trivial task. In this paper we present a TSM approach that handles transients implicitly by first separating the signal into a harmonic component as well as a percussive component which typically contains the transients. While the harmonic component is modified with a phase vocoder approach using a large frame size, the noise-like percussive component is modified with a simple time-domain overlap-add technique using a short frame size, which preserves the transients to a high degree without any explicit transient detection.
Overview
CoChat Summary
TL;DR
The paper proposes a time-scale modification method that separates music into harmonic and percussive components, applies a phase vocoder to the harmonic part and overlap-add to the percussive part, then recombines them. In a listening experiment, this harmonic-percussive approach preserved transients well and was significantly better than phase vocoder, WSOLA, and a transient-handling phase vocoder, and comparable to commercial élastique.
Key findings
- The proposed harmonic-percussive (HP) method and commercial élastique (EL) performed significantly better than the remaining three tested algorithms: phase vocoder with identity phase locking (PV), Nagel and Walther’s transient-preserving phase vocoder (NW), and WSOLA (WS).
- For highly percussive excerpts such as Bongo, CastanetsViolin, and DrumSolo, HP outperformed all other tested TSM procedures; it also scored best for complex mixtures Jazz, Pop, and Stepdad.
- HP performed significantly better than PV on average and was comparable in quality to EL on the tested dataset.
- HP performed worse for SynthMono, attributed to phase vocoder “phasiness,” and for SingingVoice, where the harmonic-percussive separation was problematic because human voice is neither clearly harmonic nor clearly percussive; for SingingVoice, PV scored better than HP.
Methods
- The proposed method first separates the input into harmonic and percussive components using an STFT-based median-filtering harmonic-percussive separation with binary masks. The harmonic component is modified with a phase vocoder using a large frame size of 4096 samples at 22050 Hz, identity phase locking, and amplitude rescaling; the percussive component is modified with time-domain overlap-add using a small frame size of 256 samples. The two modified components are superimposed.
- Evaluation was an online listening experiment comparing five TSM algorithms: HP, EL, PV, NW, and WS. The dataset comprised ten short audio excerpts covering purely percussive, monophonic, highly polyphonic, synthetic, and professionally mixed studio recordings. Each excerpt was stretched with a moderate and a stronger stretching factor, yielding 100 test items total.
- Twenty-two people, most with a background in audio signal processing, rated the five TSM results per excerpt on a five-point mean opinion score scale from 1 (bad) to 5 (excellent). Results are reported as mean opinion scores.
Limitations
- The paper states that systematic experiments on choosing optimal values for the STFT frame size and median filter length are left as future work; it also notes phasiness artifacts in the harmonic component for SynthMono and problematic harmonic-percussive separation for SingingVoice, with future work needed to reduce phasiness and better handle signals that are neither clearly harmonic nor clearly percussive.
- The text makes evident that the listening evaluation used only ten excerpts and 22 listeners, and although it reports “significantly” better performance, no statistical test details are provided.
- The text makes evident that the reported parameter settings are fixed for the tested sampling rates, and the paper notes that only the ratio between sampling rate and frame size was found important for separation, while optimal parameter choices remain unexamined.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 Valid · 4 Not valid · 10 Not checked · 33 Own results and interpretations
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As for the percussive component, we found out that applying a simple time-domain overlap-add method (OLA) yields surprisingly good TSM results when using a very small frame size.”
§ Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation > I. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although OLA is considered a poor TSM technique in particular for harmonic sounds, it is capable of preserving the sound of transients without any explicit transient detection.”
- Also stated in: § III. TIME-SCALE MODIFICATION · plain
“The conclusion is that OLA can be used to modify noise-like signals, while preserving transients without any explicit transient detection step.”
§ Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation > I. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption not shown here.
- Also stated in: § III. TIME-SCALE MODIFICATION · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“One main observation is that the percussive component contains, besides other noise-like sounds, all the transients.”
§ Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation > I. INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This also explains why the native phase vocoder PV scores better than HP for this excerpt.”
§ Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation > IV. EVALUATION
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In further experiments it showed that for obtaining such results only the ratio between the sampling rate and the frame size is important.”
§ Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation > III. TIME-SCALE MODIFICATION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
25 more claims
Create a free account to see all 47 claims and every source verdict.
- It may state two separate claims as one. 15
- Probably not a substantial claim. 4
- The quote only partly states this claim. 1
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
“While OLA is unsuited for modifying audio signals with harmonic content, it delivers high quality results for purely percussive signals, as is the case with drum or castanet recordings [8].”
From A Review of Time-Scale Modification of Music Signals (2016) · cites this paper as [8]
In the citing paper
“However, it is possible to substantially reduce artifacts by combining different TSM approaches.”
From A Review of Time-Scale Modification of Music Signals (2016) · cites this paper as [8]
In the citing paper
“The idea of a recent TSM procedure proposed in [8] is to combine PV-TSM and OLA in order to modify both harmonic and percussive sound components with high quality.”
From A Review of Time-Scale Modification of Music Signals (2016) · cites this paper as [8]
In the citing paper
“By treating the two components independently with the two different TSM procedures, their respective characteristics can be preserved to a high degree.”
From A Review of Time-Scale Modification of Music Signals (2016) · cites this paper as [8]
In the citing paper
“Established TSM methods have predominantly focused on the separation and accurate manipulation of sinusoidal and transient components of sounds [13]-[15].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [13]
5 more claims about this paper
Create a free account to see every claim other papers make about this one.
Citations
Cites
Signal estimation from modified short-time Fourier transform
Griffin, D., & Lim, J. (1984) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1984.1164317 · Cited by 2,095
Improved phase vocoder time-scale modification of audio
Laroche, J., & Dolson, M. (1999) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.759041 · Cited by 276
Flanagan, J. L., & Golden, R. M. (1966) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1966.tb01706.x · Cited by 226
Verhelst, W., & Roelands, M. (1993) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1993.319366 · Cited by 217
A Real-Time Equalizer Of Harmonic And Percussive Components In Music Signals.
Ono, N., Miyamoto, K., Kameoka, H., & Sagayama, S. (2008) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1415043 · Cited by 61
Time-scale modification of audio signals using enhanced WSOLA with management of transients
Grofit, S., & Lavner, Y. (2008) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2007.909444 · Cited by 34
(2009) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)
(2010) · Arrow@dit (Dublin Institute of Technology)
(2002) · Journal of the Audio Engineering Society
Research works printed but not linked: 4
- driedger, 0, Accompanying website Improving time-scale modification of music signals using harmonic-percussive separation
- 2013, �lastique time stretching & pitch shifting SDKs
- saint-arnaud, 1998, chapter Analysis and Synthesis of Sound Textures, 293
- duxbury, 2001, Separation of transient information in audio using multiresolution analysis techniques, Proc COST G-6 Conference on Digital Audio Effects (DAFX-01)
Cited by
An Ensemble of Convolutional Neural Networks for Audio Classification
Nanni, L., Maguolo, G., Brahnam, S., & Paci, M. (2021) · Applied Sciences · doi:10.3390/app11135796 · Cited by 115
A Review of Time-Scale Modification of Music Signals
Driedger, J., & Müller, M. (2016) · Applied Sciences · doi:10.3390/app6020057 · Cited by 71
Self-citationExtending Harmonic-Percussive Separation Of Audio Signals.
Driedger, J., Müller, M., & Disch, S. (2014) · doi:10.5281/zenodo.1415225 · Cited by 69
Self-citationAudiogmenter: a MATLAB toolbox for audio data augmentation
Maguolo, G., Paci, M., Nanni, L., & Bonan, L. (2021) · Applied Computing and Informatics · doi:10.1108/aci-03-2021-0064 · Cited by 39
(2014)
Self-citationEffect of Prosody Modification on Children's ASR
Shahnawazuddin, S., Adiga, N., & Kathania, H. K. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2756347 · Cited by 38
Audio Time Stretching Using Fuzzy Classification of Spectral Bins
Damskägg, E.-P., & Välimäki, V. (2017) · Applied Sciences · doi:10.3390/app7121293 · Cited by 34
Phase-aware Harmonic/percussive Source Separation via Convex Optimization
Masuyama, Y., Yatabe, K., & Oikawa, Y. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683821 · Cited by 27
From raw audio to a seamless mix: creating an automated DJ system for Drum and Bass
Veire, L. V., & Bie, T. D. (2018) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-018-0134-8 · Cited by 23
Explicit Pitch Mapping for Improved Children’s Speech Recognition
Kathania, H. K., Ahmad, W., Shahnawazuddin, S., & Samaddar, A. B. (2017) · Circuits Systems and Signal Processing · doi:10.1007/s00034-017-0652-0 · Cited by 22
Průša, Z., & Holighaus, N. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081353 · Cited by 22
Using Deep Neural Networks for On-Load Tap Changer Audio-Based Diagnostics
Secic, A., Krpan, M., & Kuzle, I. (2021) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2021.3121472 · Cited by 20
Shahnawazuddin, S., Adiga, N., Sai, B. T., Ahmad, W., & Kathania, H. K. (2019) · Digital Signal Processing · doi:10.1016/j.dsp.2019.06.015 · Cited by 20
Ahmad, W., Shahnawazuddin, S., Kathania, H. K., Pradhan, G., & Samaddar, A. B. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-302 · Cited by 18
Sabat, M., Gouyette, H., Gaucher, Q., Espejo, M. L., David, S. V., Norman-Haignere, S., & Boubenec, Y. (2025) · Current Biology · doi:10.1016/j.cub.2025.11.011 · Cited by 17
Source Separation of Piano Concertos Using Musically Motivated Augmentation Techniques
Özer, Y., & Müller, M. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3356980 · Cited by 15
Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery
Drossos, K., Magron, P., Mimilakis, S. I., & Virtanen, T. I. (2018) · International Workshop on Acoustic Signal Enhancement · doi:10.1109/iwaenc.2018.8521371 · Cited by 15
Hoang, T., Fahier, N., & Fang, W.-C. (2019) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2019.8919049 · Cited by 15
Music genre classification using multi-modal deep learning based fusion
Wadhwa, L., & Mukherjee, P. (2021) · Grace Hopper Celebration India (GHCI) · doi:10.1109/ghci50508.2021.9514020 · Cited by 14
Martínez, F. D. G., Carabias-Orti, J. J., Canadas-Quesada, F. J., Ruiz-Reyes, N., Martínez-Muñoz, D., & Galán, S. G. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2023.109811 · Cited by 13
Improving the Robustness of DTW to Global Time Warping Conditions in Audio Synchronization
Kraprayoon, J., Pham, A., & Tsai, T. J. (2024) · Applied Sciences · doi:10.3390/app14041459 · Cited by 13
Singing Expression Transfer from One Voice to Another for a Given Song
Yong, S., & Nam, J. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461793 · Cited by 12
Speech Time-Scale Modification With GANs
Cohen, E., Kreuk, F., & Keshet, J. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3164361 · Cited by 13
Müller, M. (2021) · Signals · doi:10.3390/signals2020018 · Cited by 10
Park, J., Shin, J., & Lee, K. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2681742 · Cited by 10
Bhattacharjee, M., Prasanna, S. R. M., & Guha, P. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3164199 · Cited by 9
Kek, X. Y., Chin, C. S., & Li, Y. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003150 · Cited by 8
Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise
Fierro, L., & Välimäki, V. (2023) · Journal of the Audio Engineering Society · doi:10.17743/jaes.2022.0077 · Cited by 4
Adaptive Pitch-Shifting with Applications to Intonation Adjustment in a Cappella Recordings
Rosenzweig, S., Schwär, S., Driedger, J., & Müller, M. (2021) · International Conference on Digital Audio Effects (DAFx) · doi:10.23919/dafx51585.2021.9768268 · Cited by 7
Self-citationTime-Scale Modification Using Fuzzy Epoch-Synchronous Overlap-Add (FESOLA)
Roberts, T., & Paliwal, K. K. (2019) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2019.8937258 · Cited by 7
Santos, C. H. T., & Pereira, V. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3171969 · Cited by 6
Instantaneous doppler signature extraction from within a spectrogram image of a VHF band
Ptak, P., Hartikka, J., Ritola, M., & Kauranne, T. (2016) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2015.150077 · Cited by 6
Miao, X., & McLoughlin, I. V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.09003 · Cited by 6
Puppet DubbingPreprint
Fried, O., & Agrawala, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04285 · Cited by 6
Effect of Speech Modification on Wav2Vec2 Models for Children Speech Recognition
Sinha, A., Singh, M., Kadiri, S. R., Kurimo, M., & Kathania, H. K. (2024) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom60851.2024.10631626 · Cited by 5
Lordelo, C., Benetos, E., Dixon, S., & Ahlbäck, S. (2019) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2019.8937079 · Cited by 5
A time-scale modification dataset with subjective quality labels
Roberts, T., & Paliwal, K. K. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0001567 · Cited by 4
Improving Phase-Vocoder-Based Time Stretching by Time-Directional Spectrogram Squeezing
Akaishi, N., Yatabe, K., & Oikawa, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095348 · Cited by 4
Structural Segmentation and Labelling of Tabla Solo Performances
Gowriprasad, R., Aravind, R., & Murthy, H. A. (2022) · Journal of New Music Research · doi:10.1080/09298215.2023.2265912 · Cited by 3
Musically Informed Audio Decomposition
Müller, M. (2015) · Springer eBooks · doi:10.1007/978-3-319-21945-5_8 · Cited by 3
Make Your Own Accompaniment: Adapting Full-Mix Recordings To Match Solo-Only User Recordings.
Tsai, T. J., Tjoa, S. K., & Müller, M. (2017) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · doi:10.5281/zenodo.1417017 · Cited by 3
Gongora, D., Konyo, M., Nagano, H., & Tadokoro, S. (2019) · IEEE Transactions on Haptics · doi:10.1109/toh.2019.2957792 · Cited by 3
An objective measure of quality for time-scale modification of audio
Roberts, T., & Paliwal, K. K. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0003753 · Cited by 2
Noise Morphing for Audio Time Stretching
Moliner, E., Fierro, L., Wright, A., Hämäläinen, M. S., & Välimäki, V. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3386118 · Cited by 2
Extreme Audio Time Stretching Using Neural Synthesis
Fierro, L., Wright, A., Välimäki, V., & Hämäläinen, M. (2023) · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp49357.2023.10094738 · Cited by 2
Wang, T., Yi, J., Fu, R., Qiang, C., Chong, D., Wang, C., Dai, D., Wen, Z., & Tao, J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3626793 · Cited by 2
Evaluating the effects of modified speech on perceptual speaker identification performance
O’Brien, B., Meunier, C., & Ghio, A. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-463 · Cited by 2
Vibrotactile Feedback Improves Collision Detection in Fast Playback of First-Person View Videos
Gongora, D., Nagano, H., Konyo, M., & Tadokoro, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-93399-3_54 · Cited by 2
Impulsive spike enhancement on gamelan audio using harmonic percussive separation
Solekhan, S., Suprapto, Y. K., & Wirawan, W. (2019) · International Journal of Electrical and Computer Engineering (IJECE) · doi:10.11591/ijece.v9i3.pp1700-1710 · Cited by 1
Kobayashi, K., Masuyama, Y., Yatabe, K., & Oikawa, Y. (2021) · Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi · doi:10.1250/ast.42.261 · Cited by 1
Sitrano: A Matlab App for Sines-Transients-Noise Decomposition of Audio Signals
Fierro, L., & Välimäki, V. (2021) · International Conference on Digital Audio Effects (DAFx) · doi:10.23919/dafx51585.2021.9768212 · Cited by 1
Akaishi, N., Yamada, K., & Yatabe, K. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3459811 · Cited by 1
Constraint-Based Time-Scale Modification of Music Recordings for Noise Beautification
Müller, M., Hedwig, H., Zalkow, F., & Popescu, S. (2018) · Applied Sciences · doi:10.3390/app8030436
Tmeme, A., & Hadi, A. S. (2017)
Shahnawazuddin, S., Ahmad, W., Kathania, H. K., Adiga, N., & Sai, B. T. (2019) · National Conference on Communications · doi:10.1109/ncc.2019.8732255
Dense-Sparse Dynamic Time Warping for Customizing Piano Concerto Accompaniments
Tsai, T. J., Dey, K., Özer, Y., & Müller, M. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890080
Sinha, A., Singh, M., Kadiri, S. R., Kathania, H. K., & Kurimo, M. (2025) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco63237.2025.11226615
A general algorithm for the real-time emulationof pitched musical instruments and the singing voice
dos SANTOS, C. A. A. S. (2023) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas)
Pancaldi, F., & Dibiase, L. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109864
A Frequency-Structured Dilated Conformer Architecture for Full-Kit Automatic Drum Transcription
Khanal, R., & Lee, J. (2026) · Applied Sciences · doi:10.3390/app16136746
Akaishi, N., Holighaus, N., & Yatabe, K. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3711772
Sharma, N., Potadar, S., Chetupalli, S. R., & Sreenivas, T. V. (2017) · 2017 Twenty-third National Conference on Communications (NCC) · doi:10.1109/ncc.2017.8077073 · Cited by 3
Stereo Time-Scale Modification Using Sum and Difference Transformation
Roberts, T., & Paliwal, K. K. (2018) · 2018 12th International Conference on Signal Processing and Communication Systems (ICSPCS) · doi:10.1109/icspcs.2018.8631776 · Cited by 1
Metadata
- Authors
- Driedger, J., Muller, M., & Ewert, S.
- Affiliations
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- International Audio Laboratories Erlangen, The International Audio Laboratories Erlangen are a joint institution of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Fraunhofer Institut für Integrierte Schaltungen (IIS), Erlangen, Germany
- Queen Mary University of London (GB)
- ORCID
- Ewert, Sebastian
- Venue
- IEEE Signal Processing Letters
- Published
- 2014-01
- Volume
- 21
- Issue
- 1
- Pages
- 105-109
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W1979932443
- Cited by
- 64
- References
- 9
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 5 · 2021: 9 · 2020: 1 · 2019: 11 · 2018: 6 · 2017: 9 · 2016: 2 · 2015: 2 · 2014: 2
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- green
- qmro.qmul.ac.uk
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/L019981/1, EP/J010375/1)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Arts and Humanities › Music › Music Technology and Sound Studies
- Keywords
- time-scale modification, music signals, phase vocoder, overlap-add, perceptual quality, frame size
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-11-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-11-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
