Real-time unsupervised speaker change detection
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: 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: 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: 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: Repeating pattern discovery and structure analysis from acoustic music data
abstract: Music and songs usually have repeating patterns and prominent structure. The automatic extraction of such repeating patterns and structure is useful for further music summarization, indexing and retrieval. In this paper, an effective approach of repeating pattern discovery and structure analysis of acoustic music data is proposed. In order to represent the melody similarity more accurately, in our approach, Constant Q transform is utilized in feature extraction and a novel similarity measure between musical features is proposed. From the self-similarity matrix of the music, an adaptive method is then presented to extract all significant repeating patterns. Based on the obtained repetitions, musical structure is further analyzed using a few heuristic rules. Finally, an optimization-based approach is proposed to determine the accurate boundary of each musical section. Evaluations on various music pieces indicate our approach is promising.

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: 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: Efficient particle filtering using RANSAC with application to 3D face tracking
abstract: Particle filtering is a very popular technique for sequential state estimation. However, in high-dimensional cases where the state dynamics are complex or poorly modeled, thousands of particles are usually required for real applications. This paper presents a hybrid sampling solution that combines RANSAC and particle filtering. In this approach, RANSAC provides proposal particles that, with high probability, represent the observation likelihood. Both conditionally independent RANSAC sampling and boosting-like conditionally dependent RANSAC sampling are explored. We show that the use of RANSAC-guided sampling reduces the necessary number of particles to dozens for a full 3D tracking problem. This method is particularly advantageous when state dynamics are poorly modeled. We show empirically that the sampling efficiency (in terms of likelihood) is much higher with the use of RANSAC. The algorithm has been applied to the problem of 3D face pose tracking with changing expression. We demonstrate the validity of our approach with several video sequences acquired in an unstructured environment.

title: Constrained planar motion analysis by decomposition
abstract: General SFM methods give poor results for images captured by constrained motions such as planar motion. In this paper, we propose new SFM algorithms for images captured under a common but constrained planar motion: the image plane is perpendicular to the motion plane. We show that a 2D image captured under such constrained planar motion can be decoupled into two 1D images: one 1D projective and one 1D affine. We then introduce the 1D affine camera model for completing 1D camera models. Next, we describe new subspace reconstruction methods, and apply these methods to the images captured by concentric mosaics, which undergo a special case of constrained planar motion. Finally, we demonstrate both in theory and experiments the advantage of the decomposition method over the general SFM methods by incorporating the constrained motion into the earliest stage of motion analysis.