Smart ambient sound analysis via structured statistical modeling

Jialie Shen, Liqiang Nie, Tat Seng Chua

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review


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.

Original languageEnglish
Title of host publicationMultiMedia Modeling
Subtitle of host publication22nd International Conference, MMM 2016, Proceedings
EditorsQi Tian, Nicu Sebe, Guo-Jun Qi, Benoit Huet, Richang Hong, Xueliang Liu
Place of PublicationMiami; United States
PublisherSpringer-Verlag London Ltd.
Number of pages13
ISBN (Print)9783319276731
Publication statusPublished - 2016
Externally publishedYes
Event22nd International Conference on MultiMedia Modeling, MMM 2016 - Miami, United States
Duration: 4 Jan 20166 Jan 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on MultiMedia Modeling, MMM 2016
Country/TerritoryUnited States


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