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.
- Citation
- BibTeX
Kruspe, A., Zapf, D. & Lukashevich, H.,
(2017).
Automatic speech/music discrimination for broadcast signals.
In:
Eibl, M. & Gaedke, M.
(Hrsg.),
INFORMATIK 2017.
Gesellschaft für Informatik, Bonn.
(S. 151-162).
DOI: 10.18420/in2017_10
@inproceedings{mci/Kruspe2017,
author = {Kruspe, Anna AND Zapf, Dominik AND Lukashevich, Hanna},
title = {Automatic speech/music discrimination for broadcast signals},
booktitle = {INFORMATIK 2017},
year = {2017},
editor = {Eibl, Maximilian AND Gaedke, Martin} ,
pages = { 151-162 } ,
doi = { 10.18420/in2017_10 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
author = {Kruspe, Anna AND Zapf, Dominik AND Lukashevich, Hanna},
title = {Automatic speech/music discrimination for broadcast signals},
booktitle = {INFORMATIK 2017},
year = {2017},
editor = {Eibl, Maximilian AND Gaedke, Martin} ,
pages = { 151-162 } ,
doi = { 10.18420/in2017_10 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
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More Info
DOI: 10.18420/in2017_10
ISBN: 978-3-88579-669-5
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2017
Language:
(en)

Collections
- P275 - INFORMATIK 2017 [266]
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