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Over the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for efficient automated singer recognition. HSI uses multiple low-level features extracted from both vocal and nonvocal music segments to enhance the identification process; it achieves this via a hybrid architecture that builds profiles of individual singer characteristics based on statistical mixture models. An extensive experimental study on a large music database demonstrates the superiority of our method over state-of-the-art approaches in terms of effectiveness, efficiency, scalability, and robustness. Categories and Subject Descriptors: H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--Retrieval models; H.3.4 [Information Storage and Retrieval]: Systems and Software Performance evaluation efficiency and effectiveness; H.5.5 [Information Interfaces and Presentation]: Sound and Music Computing--Modeling; J.5 [Arts and Humanities]: Performing arts--(e.g, dance, music) General Terms: Algorithms, Experimentation, Performance Additional Key Words and Phrases: Music retrieval, singer identification, Gaussian mixture models, classification, EM algorithm, statistical modeling, evaluation ACM Reference Format: Shen, J., Shepherd, J., Cui, B., and Tan, K.-L. 2009. A novel framework for efficient automated singer identification in large music databases. ACM Trans. Inform. Syst. 27, 3, Article 18 (May 2009), 31 pages. DOI = 10.1145/1508850.1508856 http://doi.acm.org/10.1145/1508850.1508856

Event detection is one of the most fundamental components for various kinds of domain applications of video information system. In recent years, it has gained a considerable interest of practitioners and academics from different areas. While detecting video event has been the subject of extensive research efforts recently, much less existing approach has considered multimodal information and related efficiency issues. In this paper, we use a subspace selection technique to achieve fast and accurate video event detection using a subspace selection technique. The approach is capable of discriminating different classes and preserving the intramodal geometry of samples within an identical class. With the method, feature vectors presenting different kind of multi data can be easily projected from different identities and modalities onto a unified subspace, on which recognition process can be performed. Furthermore, the training stage is carried out once and we have a unified transformation matrix to project different modalities. Unlike existing multimodal detection systems, the new system works well when some modalities are not available. Experimental results based on soccer video and TRECVID news video collections demonstrate the effectiveness, efficiency and robustness of the proposed MMP for individual recognition tasks in comparison to the existing approaches. Index Terms--Multimodule, semantic event video detection.

Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors--one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis tools. Aiming at bridging this gap and generalizing conventional statistical tools over tensors, this paper proposes a decoupled probabilistic algorithm, which is named Bayesian tensor analysis (BTA). Theoretically, BTA can automatically and suitably determine dimensionality for different modalities of tensor data. With BTA, a collection of 3-D faces can be well modeled. Empirical studies on expression retargeting also justify the advantages of BTA. Index Terms--Bayesian inference, Bayesian tensor analysis, face expression synthesis, 3-D face.