Repeating pattern discovery from acoustic musical signals
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: 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: 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: 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: 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.

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: 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.