Repeating pattern discovery and structure analysis from acoustic music data
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: Web object indexing using domain knowledge
abstract: A web object is defined to represent any meaningful object embedded in web pages (e.g. images, music) or pointed to by hyperlinks (e.g. downloadable files). In many cases, users would like to search for information of a certain 'object', rather than a web page containing the query terms. To facilitate web object searching and organizing, in this paper, we propose a novel approach to web object indexing, by discovering its inherent structure information with existed domain knowledge. In our approach, first, Layered LSI spaces are built for a better representation of the hierarchically structured domain knowledge, in order to emphasize the specific semantics and term space in each layer of the domain knowledge. Meanwhile, the web object representation is constructed by hyperlink analysis, and further pruned to remove the noises. Then an optimal matching between the web object and the domain knowledge is performed, in order to pick out the structure attributes of the web object from the knowledge. Finally, the obtained structure attributes are used to re-organize and index the web objects. Our approach also indicates a new promising way to use trust-worthy Deep Web knowledge to help organize dispersive information of Surface Web.

title: Universal Background Models for Real-time Speaker Change Detection
abstract: This paper addresses the problem of real-time speaker change detection in TV news broadcast, in which no prior knowledge on speakers is assumed. To remove the unreliable frames and background frames in the speech stream, we propose a new approach for feature categorization based on Gaussian Mixture Model - Universal Background Model (GMM-UBM). The feature vectors are categorized into three sets, which include reliable speech, doubtful speech and unreliable speech. Then a novel distance measure is presented correspondingly for real-time speaker change detection. Extensive experiments demonstrate its good performance, and intrinsic difficulties on real-time speaker change detection are discussed as well in this paper.

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: Dominant Feature Vectors Based Audio Similarity Measure
abstract: 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: Repeating pattern discovery from acoustic musical signals
abstract: Music pieces are typically repetitive. The automatic extraction of repeating patterns is useful for music summary, indexing and retrieval. An effective approach for repeating pattern discovery is proposed. In order to represent the melody similarity more accurately, a constant Q transform is used for feature extraction and a novel similarity measure between musical features is proposed. From the self-similarity matrix of the music, an adaptive method is used to extract all the significant repeating patterns. Experiments on pop music indicate the approach is promising.

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: A two level approach for scene recognition
abstract: Classifying pictures into one of several semantic categories is a classical image understanding problem. In this paper, we present a stratified approach to both binary (outdoor-indoor) and multiple category of scene classification. We first learn mixture models for 20 basic classes of local image content based on color and texture information. Once trained, these models are applied to a test image, and produce 20 probability density response maps (PDRM) indicating the likelihood that each image region was produced by each class. We then extract some very simple features from those PDRMs, and use them to train a bagged LDA classifier for 10 scene categories. For this process, no explicit region segmentation or spatial context model are computed. To test this classification system, we created a labeled database of 1500 photos taken under very different environment and lighting conditions, using different cameras, and from 43 persons over 5 years. The classification rate of outdoor-indoor classification is 93.8%, and the classification rate for 10 scene categories is 90.1%. As a byproduct, local image patches can be contextually labeled into the 20 basic material classes by using loopy belief propagation (Yedidia et al., 2001) as an anisotropic filter on PDRMs, producing an image-level segmentation if desired.

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.