NDLI: Vocal Characteristics Classification of Audio Segments: An Investigation of the Influence of Accompaniment Music on Low-Level Features

Content Provider	IEEE Xplore Digital Library
Author	Gartner, D. Dittmar, C.
Copyright Year	2009
Abstract	The characteristics of vocal segments in music are an important cue for automatic, content-based music recommendation, especially in the urban genre. In this paper, we investigate the classification of audio segments into singing and rap, using low-level acoustic features and a Bayesian classifier. GMMs are used as parametric clustering method to describe the distribution of the training data. Different low-level audio features features are assessed with regard to their ability to perform this task. Further, we study the influence of the accompaniment music on the performance of the classifier. We find that the performance of the classifier also depends on the background music of the training and testing data. Some features, even if they yielded useful results on isolated vocal tracks, are not able to preserve information about the vocal content when mixed with background music, thus leading to erroneous classifications.
Starting Page	583
Ending Page	589
File Size	196744
Page Count	7
File Format	PDF
ISBN	9780769539263
DOI	10.1109/ICMLA.2009.40
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	rap Speech analysis Instruments GMM Mel frequency cepstral coefficient Support vector machines Cepstral analysis Hidden Markov models Music Support vector machine classification Detectors low-level features sing Autoregressive processes music information retrieval
Content Type	Text
Resource Type	Article

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