NDLI: Experimental Investigation of Transitions for Mixed Speech and Music Playlist Generation

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Experimental Investigation of Transitions for Mixed Speech and Music Playlist Generation

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Experimental Investigation of Transitions for Mixed Speech and Music Playlist Generation

Content Provider	ACM Digital Library
Author	Lukács, Gergely Jani, Mátyás Takács, György
Abstract	The Internet and mobile phones allow customizing media content individually. In case of a radio program, beside a good selection of content, the acoustic and semantic quality of the transitions between pieces of audio material also play a significant role influencing the listening experience. Our goal is to automate radio program editing for individually customized (Internet) radio programmes, containing both pieces of speech and music. This paper deals with the acoustic features of speech to music transitions. In the course of the study, based on professionally edited, authentic radio programmes, a set of audio test data -- pairs of speech and music -- was selected and specially prepared. A subjective opinion test with a group of users for rating the quality of the transitions was conducted and acoustic features were extracted from both the pieces of speech and music. The collected data was analysed using data mining methods. The first analysis compared data from the subjective opinion test with data from professional editors. The correlation was found to be significant, i.e., potential listeners had similar judgements to those of professional radio editors. The central, second analysis aimed at predicting the subjective acoustic quality of a speech to music transition based on automatically extractable features of speech and music. The attributes found to be significantly influencing the acoustic quality are related to the tempo and Mel spectrum values. The numeric quality of the match can be predicted with a cross-correlation of 0.55 and the digitised quality with a performance of 75%.
Starting Page	392
Ending Page	398
Page Count	7
File Format	PDF
ISBN	9781450327824
DOI	10.1145/2578726.2578777
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-04-01
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Audio features Music Speech Data mining Playlist generation
Content Type	Text
Resource Type	Article

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