Abstract
In this paper we present an effective approach which addresses the issue of speech/music discrimination. Our architecture focuses on the matter from the scope of improving the performance of a speech recognition system by excluding the processing of information which is not speech. Multiresolution analysis is applied to the input signal while the most significant statistical features are calculated over a predefined texture size. These characteristics are then modeled using a state of the art technique for probability density function estimation, Gaussian mixture models (GMM). A classification scheme consisting of a conventional maximum likelihood decision methodology constitutes the next step of our implementation. Despite the fact that our system is based solely on wavelet signal processing, it demonstrated very good performance achieving 91.8% recognition rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Scheirer, E., Slaney, M.: Construction and evaluation of a robust multifeature speech/music discriminator. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 1331–1334 (1997)
El-Maleh, K., Klein, M., Petrucci, G., Kabal, P.: Speech/music discrimination for multimedia applications. In: IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 6, pp. 2445–2448 (2000)
Pinquier, J., Rouas, J.-L., Andre-Obrecht, R.: A fusion study in speech/music classification. In: International Conference in Multimedia and Expo., vol. 2, pp. 409–412 (2003)
Tzanetakis, G., Essl, G., Cook, P.R.: Audio analysis using the wavelet transform. In: International Conference on Acoustics and Music: Theory and Applications (AMTA) (2001)
Kashif Saeed Khan, M., Al-Khatib, W.G., Moinuddin, M.: Automatic classification of speech and music using neural networks. In: 2nd ACM international workshop on Multimedia databases, pp. 94–99 (2004)
Didiot, E., Illina, I., Mela, O., Haton, J.P., Fohr, D.: A wavelet-based parameterization for speech/music segmentation. In: International Conference on Spoken Language Processing, pp. 653–656 (2006)
Torrence, C., Compo, G.P.: A practical guide to wavelet analysis. Bulletin of the American Meteorological society 79, 61–78 (1998)
EBU.: SQAM - CD: Sound quality assessment material, Polygram Cat. No 422 204-2, European Broadcasting Union (EBU) (1988)
EBU.: Sound quality assessment material, Recordings for subjective tests – Users/ handbook for the EBU-SQAM Compact Disc, Tech 3253, European Broadcasting Union (EBU) (1988)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2, 674–693 (1989)
Foote, J.: An overview of audio information retrieval. ACM Multimedia Systems 7, 2–10 (1999)
Nabney, I.: Netlab: Algorithms for Pattern Recognition. Springer, London (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ntalampiras, S., Fakotakis, N. (2008). Speech/Music Discrimination Based on Discrete Wavelet Transform. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-87881-0_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87880-3
Online ISBN: 978-3-540-87881-0
eBook Packages: Computer ScienceComputer Science (R0)