The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise
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In the citing paper
“The weakness of established TSM methods, often based on a phase vocoder structure, lies in the poor description and scaling of the transient and noise components, or nuances, of a sound.”
From Extreme Audio Time Stretching Using Neural Synthesis (2023) · cites this paper as [15]
In the citing paper
“The STN separation method proposed by Fierro and Välimäki [15] decomposes a sound into three abstract classes: sines (tonal content), transients (impulsive events), and noise (sound nuances).The decomposition is achieved through soft spectral masks that are derived from the signal spectrogram and allow for perfect reconstruction.”
From Extreme Audio Time Stretching Using Neural Synthesis (2023) · cites this paper as [15]
In the citing paper
“In this letter, audio signals are decomposed into these three components via soft spectral masking of their spectrograms, which leads to a fuzzy decomposition with perfect reconstruction and is the best method to date for this specific task [22].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [22]
In the citing paper
“The use of a three-way decomposition to isolate the noise component from sines and transients [19]-[22], in combination with phase randomization [23], [24] in the resynthesis process, showed a first improvement in the quality of the stretched noise component [4], [22].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [22]
In the citing paper
“As a consequence of the fuzzy classification, each time-frequency bin can belong to two classes simultaneously: to the sine and noise, or to the transient and noise classes [22].”
From Noise Morphing for Audio Time Stretching (2024) · cites this paper as [22]
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Citations
Cites
Improving Time-Scale Modification of Music Signals Using Harmonic-Percussive Separation
Driedger, J., Muller, M., & Ewert, S. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2294023 · Cited by 64
Audio Time Stretching Using Fuzzy Classification of Spectral Bins
Damskägg, E.-P., & Välimäki, V. (2017) · Applied Sciences · doi:10.3390/app7121293 · Cited by 34
Self-citation
Cited by
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
Self-citationExtreme 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
Self-citation
Metadata
- Authors
- Fierro, L., & Välimäki, V.
- Venue
- Journal of the Audio Engineering Society
- Publisher
- Audio Engineering Society
- Published
- 2023-07-10
- Volume
- 71
- Issue
- 7/8
- Pages
- 468-480
- ISSN
- 1549-4950
- Cited by
- 4
- References
- 2
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise. https://app.cochat.ai/papers/10.17743/jaes.2022.0077
- The paper
- Fierro, L., & Välimäki, V. (2023). Enhanced Fuzzy Decomposition of Sound Into Sines, Transients, and Noise. Journal of the Audio Engineering Society. https://doi.org/10.17743/jaes.2022.0077
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.
