Audio Feature Extraction and Analysis for Scene Segmentation and Classification
Journal of VLSI Signal Processing Systems - special issue on multimedia signal processing
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in 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
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A flexible framework for key audio effects detection and auditory context inference
IEEE Transactions on Audio, Speech, and Language Processing
A generic audio classification and segmentation approach for multimedia indexing and retrieval
IEEE Transactions on Audio, Speech, and Language Processing
Audio Keywords Discovery for Text-Like Audio Content Analysis and Retrieval
IEEE Transactions on Multimedia
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
Correlated PLSA for image clustering
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Multimodal video concept detection via bag of auditory words and multiple kernel learning
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Fusing audio vocabulary with visual features for pornographic video detection
Future Generation Computer Systems
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This paper proposes a new approach and algorithm for the semantic concept annotation based on audio PLSA (probabilistic latent semantic analysis) model. The novelty of our approach includes two sides: Audio vocabulary construction, and audio PLSA model. In audio vocabulary construction, we first segment an audio-clip into a few homogeneous audio-segments according to its content change, which not only capture the change property of audio-clip, but also keep and present the change relation and temporal order of audio features. Then an audio vocabulary is constructed by the RPCL (rival penalized competitive learning) clustering of audio-segments. In this way, each audio-clip can be represented by a bag-of-word form. In audio PLSA model, PLSA is employed to discover the latent topics existing in audio-clips. Based on the discovered topics, the concept classification is then carried out by a support vector machine (SVM) classifier. In addition, we also combine the local features extracted by PLSA and global features in audio-clip to further improve the performance of concept annotation. The experiments are evaluated on 85 hours of audio data from the TRECVID 2005, and show the encouraging results of our approach.