Conference Proceeding Article
In this paper, we introduce a novel framework called SASA (Smart Ambient Sound Analyser) to support different ambient audio mining tasks (e.g., audio classification and location estimation). To gain comprehensive ambient sound modelling, SASA extracts a variety of acoustic features from different sound components (e.g., music, voice and background), and translates them into structured information. This significantly enhances quality of audio content representation. Further, distinguished from existing approaches, SASA’s multilayered architecture seamlessly integrates mixture models and aPEGASOS (adaptive PEGASOS) SVM algorithm into a unified classification framework. The approach can leverage complimentary strengths of both models. Experimental results based on three large test collections demonstrate the SASA’s advantages over existing methods on various analysis tasks.
Ambient intelligence, Environmental sound analysis
Computer Sciences | Databases and Information Systems
Data Management and Analytics
MultiMedia Modeling: International Conference on Multimedia Modeling 2016: Miami, FL, January 4-6
City or Country
SHEN, Jialie; NIE, Liqiang; and CHUA, Tat Seng.
Smart ambient sound analysis via structured statistical modeling. (2016). MultiMedia Modeling: International Conference on Multimedia Modeling 2016: Miami, FL, January 4-6. 231-243. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3543
Creative Commons License
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