The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Suppression of acoustic noise in speech using spectral subtraction
Abstract
A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adaptive noise cancelling: Principles and applications
Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S., Hearn, R. H., Zeidler, J. R., Eugene Dong, Jr., & Goodlin, R. C. (1975) · Proceedings of the IEEE · doi:10.1109/proc.1975.10036 · Cited by 3,939
Cited by
Speech recognition in noisy environments: A survey
Gong, Y. (1995) · Speech Communication · doi:10.1016/0167-6393(94)00059-j · Cited by 635
Transfer learning from speaker verification to multispeaker text-to-speech synthesis
Jia, Y., Zhang, Y., Weiss, R. J., Wang, Q., Shen, J., Ren, F., Chen, Z., Nguyen, P., Pang, R., Moreno, I. L., et al. (2018) · arXiv · doi:10.48550/arxiv.1806.04558 · Cited by 519
Liu, Y., Norton, J. J. S., Qazi, R., Zou, Z., Ammann, K. R., Liu, H., Yan, L., Tran, P. L., Jang, K.-I., Lee, J. W., et al. (2016) · Science Advances · doi:10.1126/sciadv.1601185 · Cited by 397
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Zhang, Z. (2015) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2015.2406332 · Cited by 388
Metadata
- Authors
- Boll, S.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1979-04
- Volume
- 27
- Issue
- 2
- Pages
- 113-120
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2128653836
- Cited by
- 4,634
- References
- 1
- FWCI
- 6.19
- Type
- journal article
- Language
- en
- Open access
- closed
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Suppression of acoustic noise in speech using spectral subtraction. https://app.cochat.ai/papers/10.1109/tassp.1979.1163209
- The paper
- Boll, S. (1979). Suppression of acoustic noise in speech using spectral subtraction. IEEE Transactions on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/tassp.1979.1163209
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.
