TY - GEN
T1 - Smart ambient sound analysis via structured statistical modeling
AU - Shen, Jialie
AU - Nie, Liqiang
AU - Chua, Tat Seng
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Ambient intelligence
KW - Environmental sound analysis
UR - http://www.scopus.com/inward/record.url?scp=84955283429&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27674-8_21
DO - 10.1007/978-3-319-27674-8_21
M3 - Conference Paper published in Proceedings
AN - SCOPUS:84955283429
SN - 9783319276731
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 231
EP - 243
BT - MultiMedia Modeling
A2 - Tian, Qi
A2 - Sebe, Nicu
A2 - Qi, Guo-Jun
A2 - Huet, Benoit
A2 - Hong, Richang
A2 - Liu, Xueliang
PB - Springer-Verlag London Ltd.
CY - Miami; United States
T2 - 22nd International Conference on MultiMedia Modeling, MMM 2016
Y2 - 4 January 2016 through 6 January 2016
ER -