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Singing Voice Detection using Modulation Frequency Features
KTH, School of Computer Science and Communication (CSC), Media Technology and Interaction Design, MID. (Sound and Music Computing)ORCID iD: 0000-0003-1679-6018
2008 (English)In: Proceedings of ISCA Tutorial and Research Workshop on Statistical and Perceptual Audition (SAPA), 2008, 7-10 p.Conference paper (Refereed)
Abstract [en]

In this paper, a feature set derived from modulation spectra is applied to the task of detecting singing voice in historical and recent recordings of Greek Rembetiko. A generalization of SVD to tensors, Higher Order SVD (HOSVD), is applied to reduce the dimensions of the feature vectors. Projection onto the “significant” principal axes of the acoustic and modulation frequency subspaces, results in a compact feature set, which is evaluated using an SVM classifier on a set of hand labeled musical mixtures. Fusion of the proposed features with MFCCs and delta coefficients reduces the optimal detection cost from 11.11% to 9.01%.

Place, publisher, year, edition, pages
2008. 7-10 p.
Keyword [en]
audio classification, modulation spectrum, singing voice activity detection.
National Category
Media Engineering
Identifiers
URN: urn:nbn:se:kth:diva-193771OAI: oai:DiVA.org:kth-193771DiVA: diva2:1040342
Conference
ISCA Tutorial and Research Workshop on Statistical and Perceptual Audition (SAPA)
Note

QC 20161101

Available from: 2016-10-27 Created: 2016-10-10 Last updated: 2016-11-17Bibliographically approved

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Holzapfel, André
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