Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107008
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Lei, B | - |
dc.creator | Mak, MW | - |
dc.date.accessioned | 2024-06-07T00:59:35Z | - |
dc.date.available | 2024-06-07T00:59:35Z | - |
dc.identifier.issn | 1380-7501 | - |
dc.identifier.uri | http://hdl.handle.net/10397/107008 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer New York LLC | en_US |
dc.rights | © Springer Science+Business Media New York 2015 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11042-015-2555-z. | en_US |
dc.subject | Feature normalization | en_US |
dc.subject | Regularized PCA-whitening | en_US |
dc.subject | Scream sound detection | en_US |
dc.subject | Sound event partitioning | en_US |
dc.title | Robust scream sound detection via sound event partitioning | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 6071 | - |
dc.identifier.epage | 6089 | - |
dc.identifier.volume | 75 | - |
dc.identifier.issue | 11 | - |
dc.identifier.doi | 10.1007/s11042-015-2555-z | - |
dcterms.abstract | This paper proposes a robust scream-sound detection scheme for acoustic surveillance applications. To enhance the discriminability between scream and non-scream sounds, a sound-event partitioning (SEP) method that facilitates the extraction of multiple acoustic vectors from a single sound event is developed. Regularized principal component analysis (PCA) and normalization are applied to the acoustic vectors, which are then classified by support vector machines (SVMs). Experimental results based on 1000 sound events show that the proposed scheme is effective even if there are severe mismatches between the training and testing conditions. The experimental results also show that the proposed scheme can reduce the equal error rate (EER) by up to 60 % when compared to a classical approach that uses mel-frequency cepstral coefficients (MFCC) as features. Extensive analyses on different processing stages of the proposed sound detection scheme also suggest that sound partitioning and feature normalization play important roles in boosting the detection performance. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Multimedia tools and applications, June 2016, v. 75, no. 11, p. 6071-6089 | - |
dcterms.isPartOf | Multimedia tools and applications | - |
dcterms.issued | 2016-06 | - |
dc.identifier.scopus | 2-s2.0-84925651793 | - |
dc.identifier.eissn | 1573-7721 | - |
dc.description.validate | 202405 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0860 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Motorola Solutions Foundation | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 6534643 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Mak_Robust_Scream_Sound.pdf | Pre-Published version | 2.64 MB | Adobe PDF | View/Open |
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