NDLI: On enabling techniques for personal audio content management

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On enabling techniques for personal audio content management

Content Provider	ACM Digital Library
Author	Partala, Juha Väyrynen, Eero Helén, Marko Lahti, Tommi Vuorinen, Olli Mäkelä, Satu-Marja Peltola, Johannes
Abstract	State-of-the-art automatic analysis tools for personal audio con-tent management are discussed in this paper. Our main target is to create a system, which has several co-operating management tools for audio database and which improve the results of each other. Bayesian networks based audio classification algorithm provides classification into four main audio classes (silence, speech, music, and noise) and serves as a first step for other subsequent analysis tools. For speech analysis we propose an improved Bayesian information criterion based speaker segmen-tation and clustering algorithm applying also a combined gender and emotion detection algorithm utilizing prosodic features. For the other main classes it is often hard to device any general and well functional pre-categorization that would fit the unforesee-able types of user recorded data. For compensating the absence of analysis tools for these classes we propose the use of efficient audio similarity measure and query-by-example algorithm with database clustering capabilities. The experimental results show that the combined use of the algorithms is feasible in practice.
Starting Page	113
Ending Page	120
Page Count	8
File Format	PDF
ISBN	9781605583129
DOI	10.1145/1460096.1460116
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Audio classification Emotion detection Speaker segmentation Query-by-example Personal audio content management
Content Type	Text
Resource Type	Article

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