Determining computable scenes in films and their structures using audio-visual memory models
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Modern Information Retrieval
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Video Scene Segmentation via Continuous Video Coherence
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Information Theoretic Clustering of Sparse Co-Occurrence Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Semantic context detection based on hierarchical audio models
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Minimal-impact audio-based personal archives
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
Creating audio keywords for event detection in soccer video
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Highlight sound effects detection in audio stream
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
A flexible framework for key audio effects detection and auditory context inference
IEEE Transactions on Audio, Speech, and Language Processing
Affective video content representation and modeling
IEEE Transactions on Multimedia
Automated high-level movie segmentation for advanced video-retrieval systems
IEEE Transactions on Circuits and Systems for Video Technology
Video summarization and scene detection by graph modeling
IEEE Transactions on Circuits and Systems for Video Technology
Towards optimal audio "keywords" detection for audio content analysis and discovery
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Bipartite isoperimetric graph partitioning for data co-clustering
Data Mining and Knowledge Discovery
A Novel Video Classification Method Based on Hybrid Generative/Discriminative Models
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Text-like segmentation of general audio for content-based retrieval
IEEE Transactions on Multimedia
Audio analysis for multimedia retrieval from a ubiquitous home
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Detecting individual role using features extracted from speaker diarization results
Multimedia Tools and Applications
Constrained co-clustering with non-negative matrix factorisation
International Journal of Business Intelligence and Data Mining
State of the art of smart homes
Engineering Applications of Artificial Intelligence
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Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. Motivated by the known limitations of traditional supervised approaches to content extraction, which are hard to generalize and require suitable training data, we propose in this paper an unsupervised approach to discover and categorize semantic content in a composite audio stream. In our approach, we first employ spectral clustering to discover natural semantic sound clusters in the analyzed data stream (e.g. speech, music, noise, applause, speech mixed with music, etc.). These clusters are referred to as audio elements. Based on the obtained set of audio elements, the key audio elements, which are most prominent in characterizing the content of input audio data, are selected and used to detect potential boundaries of semantic audio segments denoted as auditory scenes. Finally, the auditory scenes are categorized in terms of the audio elements appearing therein. Categorization is inferred from the relations between audio elements and auditory scenes by using the information-theoretic co-clustering scheme. Evaluations of the proposed approach performed on 4 hours of diverse audio data indicate that promising results can be achieved, both regarding audio element discovery and auditory scene categorization.