Dominant Feature Vectors Based Audio Similarity Measure
This paper presents an approach to extracting dominant feature vectors from an individual audio clip and then proposes a new similarity measure based on the dominant feature vectors. Instead of using the mean and standard deviation of frame features in most conventional methods, the most salient characteristics of an audio clip are represented in the form of several dominant feature vectors. These dominant feature vectors give a better description of the fundamental properties of an audio clip, especially when frame features change a lot along the time line. Evaluations on a content-based audio retrieval system indicate an obvious improvement after using the proposed similarity measure, compared with some other conventional methods.

title: P-Karaoke: personalized karaoke system
abstract: In this demonstration, a personalized Karaoke system, <i>P-Karaoke</i>, is proposed. In the P-Karaoke system, personal home videos and photographs, which are automatically selected from users' multimedia database according to their content, users' preferences or music, are utilized as the background videos of the Karaoke. The selected video clips, photographs, music and lyrics are well aligned to compose a Karaoke video, connecting by specific content-based transitions.

title: Content-based audio classification and segmentation by using support vector machines
abstract: Content-based audio classification and segmentation is a basis for further audio/video analysis. In this paper, we present our work on audio segmentation and classification which employs support vector machines (SVMs). Five audio classes are considered in this paper: silence, music, background sound, pure speech, and non- pure speech which includes speech over music and speech over noise. A sound stream is segmented by classifying each sub-segment into one of these five classes. We have evaluated the performance of SVM on different audio type-pairs classification with testing unit of different- length and compared the performance of SVM, K-Nearest Neighbor (KNN), and Gaussian Mixture Model (GMM). We also evaluated the effectiveness of some new proposed features. Experiments on a database composed of about 4- hour audio data show that the proposed classifier is very efficient on audio classification and segmentation. It also shows the accuracy of the SVM-based method is much better than the method based on KNN and GMM.

title: Automatic music video generation based on temporal pattern analysis
abstract: Music video (MV) is a short film meant to present a visual representation of a popular music song. In this paper, we present a system that automatically generates MV-like videos from personal home videos based on observations that generally there are obvious repetitive visual and aural patterns in MVs. Based on a set of video and music analysis algorithms, the automatic music video (AMV) generation system automatically extracts temporal structures of the video and music, as well as repetitive patterns in the music. And then, according to the structure and patterns, a set of highlight segments from the raw home video footage are selected, aiming at matching the visual content with the aural structure and pattern. And last, the output music video is rendered by connecting the selected highlight video segments with appropriate transition effects, accompanied with the music. Experiments show that the results are compelling and promising.

title: Unsupervised speaker segmentation and tracking in real-time audio content analysis
abstract: This paper addresses the problem of real-time speaker segmentation and speaker tracking in audio content analysis in which no prior knowledge of the number of speakers and the identities of speakers is available. Speaker segmentation is to detect the speaker change boundaries in a speech stream. It is performed by a two-step algorithm, which includes potential change detection and refinement. Speaker tracking is then performed based on the results of speaker segmentation by identifying the speaker of each segment. In our approach, incremental speaker model updating and segmental clustering is proposed, which makes the unsupervised speaker segmentation and tracking feasible in real-time processing. A Bayesian fusion method is also proposed to fuse multiple audio features to obtain a more reliable result, and different noise levels are utilized to compensate for background mismatch. Experiments show that the proposed algorithm can recall 89% of speaker change boundaries with 15% false alarms, and 76% of speakers can be unsupervised identified with 20% false alarms. Compared with previous works, the algorithm also has low computation complexity and can perform in 15% of real time with a very limited delay in analysis.

title: Real-time unsupervised speaker change detection
abstract: Information on speaker change point is very useful for speaker tracking and other applications. In this paper, we presented an effective algorithm for automatic speaker change detection based on LSP correlation analysis. Moreover, a general case is considered, in which the speaker and speaker number are both assumed unknown. The algorithm has low complexity and can be processed in real-time with a limit delay. Our experiments have shown that the algorithms produce very satisfactory results.

title: The diameter of random massive graphs
abstract: Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by so-called the “power law”. Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results show a strong evidence that the diameters of most massive graphs are about logarithm of their sizes up to a constant factor.

title: Constructive Negation Using Typed Existence Properties
abstract: A method for extracting positive information from negative goals is proposed. The method makes use of knowledge about the existence of solutions of predicates and about the types of variables. It strictly generalises earlier work in this area. It can be used as a construction rule or as a simplification rule. As a construction rule it does not involve an SLD-derivation of the negated sub-goal and so is applicable during compilation. As a simplification rule it prunes unsatisfiable goals without doing an explicit satisfiability test.

title: A Three Tiered Approach for Articulated Object Action Modeling and Recognition
abstract: Visual action recognition is an important problem in computer vision. In this paper, we propose a new method to probabilistically model and recognize actions of articulated objects, such as hand or body gestures, in image sequences. Our method consists of three levels of representation. At the low level, we first extract a feature vector invariant to scale and in-plane rotation by using the Fourier transform of a circular spatial histogram. Then, spectral partitioning [20] is utilized to obtain an initial clustering; this clustering is then refined using a temporal smoothness constraint. Gaussian mixture model (GMM) based clustering and density estimation in the subspace of linear discriminant analysis (LDA) are then applied to thousands of image feature vectors to obtain an intermediate level representation. Finally, at the high level we build a temporal multi-resolution histogram model for each action by aggregating the clustering weights of sampled images belonging to that action. We discuss how this high level representation can be extended to achieve temporal scaling in-variance and to include Bi-gram or Multi-gram transition information. Both image clustering and action recognition/segmentation results are given to show the validity of our three tiered representation.