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2017
Conference Paper
Titel

Automatic speech/music discrimination for broadcast signals

Abstract
Automatic speech/music discrimination describes the task of automatically detecting speech and music audio within a recording. This is useful for a great number of tasks in both research and industry. In particular, this approach can be used for broadcast signals (e.g. from TV or radio stations) in order to determine the amount of music played. The results can then be used for various reporting purposes (e.g. for royalty collection societies such as the German GEMA). Speech/music discrimination is commonly performed by using machine learning technologies, where models are first trained on manually annotated data, and can then be used to classify previously unseen audio data. In this paper, we give an overview over the applications and the state of the art of speech/music discrimination. Afterwards, we present our approaches based on a set of audio features, Gaussian Mixture Models and Deep Learning. Finally, we give suggestions for the direction of new research into this topic.
Author(s)
Kruspe, Anna M.
Zapf, Dominik
Lukashevich, Hanna
Hauptwerk
Informatik 2017. CD-ROM
Konferenz
Gesellschaft für Informatik (Jahrestagung) 2017
Thumbnail Image
DOI
10.18420/in2017_10
Externer Link
Externer Link
Language
English
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Fraunhofer-Institut für Digitale Medientechnologie IDMT
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