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Automatic speech/music discrimination for broadcast signals

 
: Kruspe, Anna M.; Zapf, Dominik; Lukashevich, Hanna

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Fulltext (PDF; )

Eibl, Maximilian ; Gesellschaft für Informatik -GI-, Bonn:
Informatik 2017 : 25.- 29. September 2017 Chemnitz, Deutschland
Bonn: Köllen, 2017 (GI-Edition. Proceedings 275)
ISBN: 978-3-88579-669-5
ISBN: 3-88579-669-4
pp.151-162
Gesellschaft für Informatik (Jahrestagung) <47, 2017, Chemnitz>
English
Conference Paper, Electronic Publication
Fraunhofer IDMT ()

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.

: http://publica.fraunhofer.de/documents/N-481500.html