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Proactive Caching of Music Videos Based on Audio Features, Mood, and Genre

Koch, Christian and Krupii, Ganna and Hausheer, David :
Proactive Caching of Music Videos Based on Audio Features, Mood, and Genre.
[Online-Edition: http://doi.acm.org/10.1145/3083187.3083197]
In: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys), New York, NY, USA. In: MMSys'17 . ACM , New York, NY, USA
[Conference or Workshop Item] , (2017)

Official URL: http://doi.acm.org/10.1145/3083187.3083197

Abstract

The preferred channel for listening to music is shifting towards the Internet and especially to mobile networks. Here, the overall traffic is predicted to grow by 45% annually till 2021. However, the resulting increase in network traffic challenges mobile operators. As a result, methods are researched to decrease costly transit traffic and the traffic load inside operator networks using in-network and client-side caching. Additionally to traditional reactive caching, recent works show that proactive caching increases cache efficiency. Thus, in this work, a mobile network using proactive caching is assumed. As music represents the most popular content category on YouTube, this work focuses on studying the potential of proactively caching content of this particular category using a YouTube trace containing over 4 million music video user sessions. The contribution of this work is threefold: First, music content-specific user behavior is derived and audio features of the content are analyzed. Second, using these audio features, genre and mood classifiers are compared in order to guide the design of new proactive caching policies. Third, a novel trace-based evaluation methodology for music-specific proactive in-network caching is proposed and used to evaluate novel proactive caching policies to serve either an aggregate of users or individual clients.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: Koch, Christian and Krupii, Ganna and Hausheer, David
Title: Proactive Caching of Music Videos Based on Audio Features, Mood, and Genre
Language: English
Abstract:

The preferred channel for listening to music is shifting towards the Internet and especially to mobile networks. Here, the overall traffic is predicted to grow by 45% annually till 2021. However, the resulting increase in network traffic challenges mobile operators. As a result, methods are researched to decrease costly transit traffic and the traffic load inside operator networks using in-network and client-side caching. Additionally to traditional reactive caching, recent works show that proactive caching increases cache efficiency. Thus, in this work, a mobile network using proactive caching is assumed. As music represents the most popular content category on YouTube, this work focuses on studying the potential of proactively caching content of this particular category using a YouTube trace containing over 4 million music video user sessions. The contribution of this work is threefold: First, music content-specific user behavior is derived and audio features of the content are analyzed. Second, using these audio features, genre and mood classifiers are compared in order to guide the design of new proactive caching policies. Third, a novel trace-based evaluation methodology for music-specific proactive in-network caching is proposed and used to evaluate novel proactive caching policies to serve either an aggregate of users or individual clients.

Series Name: MMSys'17
Place of Publication: New York, NY, USA
Publisher: ACM
Uncontrolled Keywords: Collaborative Filtering, Content-aware Caching, Music Analysis, Music Classification, Network Simulation, Prefetching, Proactive Caching
Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering
18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Multimedia Communications
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C3: Content-centred perspective
Event Title: Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys)
Event Location: New York, NY, USA
Date Deposited: 04 Aug 2017 09:36
DOI: 10.1145/3083187.3083197
Official URL: http://doi.acm.org/10.1145/3083187.3083197
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