A timing-based classification method for human voice in opera recordings

Maria Cristina Marinescu, Rafael Ramirez

Research output: Book chapterConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication8th International Conference on Machine Learning and Applications, ICMLA 2009
Pages577-582
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event8th International Conference on Machine Learning and Applications, ICMLA 2009 - Miami Beach, FL, United States
Duration: 13 Dec 200915 Dec 2009

Publication series

Name8th International Conference on Machine Learning and Applications, ICMLA 2009

Conference

Conference8th International Conference on Machine Learning and Applications, ICMLA 2009
Country/TerritoryUnited States
CityMiami Beach, FL
Period13/12/0915/12/09

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