DocumentCode :
3114776
Title :
A Timing-Based Classification Method for Human Voice in Opera Recordings
Author :
Marinescu, Maria-Cristina ; Ramirez, Rafael
Author_Institution :
Dept. of Comput. Sci., Univ. Carlos III de Madrid, Leganes, Spain
fYear :
2009
fDate :
13-15 Dec. 2009
Firstpage :
577
Lastpage :
582
Abstract :
The goal of this work is to identify famous tenors from commercial recordings. Our approach is based on training expressive singer-specific models and using them to classify new musical fragments interpreted by singers that perform arias from the training set. In this paper we focus on expressive timing variations and build the models by applying machine learning techniques to a body of data consisting of high-level descriptors extracted from audio recordings. The experimental results show evidence that performers can be automatically identified at a rate significantly better than random choice.
Keywords :
audio recording; learning (artificial intelligence); music; pattern classification; speech recognition; audio recordings; commercial recordings; expressive singer-specific models; expressive timing variations; high-level descriptors; human voice; machine learning techniques; musical fragments; opera recordings; tenors; timing-based classification method; Application software; Audio recording; Human voice; Machine learning; Mathematical model; Music; Performance analysis; Testing; Timbre; Timing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Applications, 2009. ICMLA '09. International Conference on
Conference_Location :
Miami Beach, FL
Print_ISBN :
978-0-7695-3926-3
Type :
conf
DOI :
10.1109/ICMLA.2009.128
Filename :
5381410
Link To Document :
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