8141740
A Vector-Based Approach to Broadcast Audio Database Indexing and Retrieval
This paper proposes a novel framework to index and retrieve audio content from broadcast database that contains both speech and music. In this framework, we model the acoustic events using hidden Markov models, which are then used to decode the audio content. The decoding results in the form of acoustic token sequence and acoustic lattice are used to generate features for indexing and retrieval with the vector space model. Experiments were carried out on the TRECVID database and the results showed that the proposed framework is effective in audio information retrieval. The results also showed that the features generated from the acoustic lattice provide more accurate information than token sequence.
16077263
The IIR Submission to CSLP 2006 Speaker Recognition Evaluation
This paper describes the design and implementation of a practical automatic speaker recognition system for the CSLP speaker recognition evaluation (SRE). The speaker recognition system is built upon four subsystems using speaker information from acoustic spectral features. In addition to the conventional spectral features, a novel temporal discrete cosine transform (TDCT) feature is introduced in order to capture long-term speech dynamic. The speaker information is modeled using two complementary speaker modeling techniques, namely, Gaussian mixture model (GMM) and support vector machine (SVM). The resulting subsystems are then integrated at the score level through a multilayer perceptron (MLP) neural network. Evaluation results confirm that the feature selection, classifier design, and fusion strategy are successful, giving rise to an effective speaker recognition system.
45515067
Evaluation Mode Research on Particle Swarm Optimization Algorithm
14334349
Learning Cascaded Reduced-Set SVMs Using Linear Programming
This paper proposes a simple and efficient detection framework that uses reduced-set kernels. We first describe our approach which reduces the number of kernels. A convex optimization method is used for calculating the reduced sets. Following this, we propose a method that optimally designs the cascade. Our experimental results indicate that our method minimizes complexity regarding the number of kernels in the cascaded structure while preserving the low error rates. Our algorithm generates the optimal weight of kernels for each cascade stage. This proposed algorithm achieves high detection-rates at low computational cost.
42292334
An Initial Framework for Collaboration-Based Component Selection
6760959
A semantic representation for image retrieval
Robust semantic labeling of image regions is a basic problem in representing and retrieving image/video content. We propose an SVM-MRF framework to model features and their spatial distributions, leading towards a "semantic" representation. Eigenfeatures of Gabor wavelet features and Gaussian mixture model are used for feature clustering. Since similar feature vectors in one cluster can come from several different semantic classes, SVM is applied to represent conditioned feature vector distributions within each cluster, and a Markov random field is used to model the spatial distributions of the semantic labels. A semantic layout representation is proposed to describe the semantics of the images. Experiments show that this method can improve semantic labeling and is useful in similarity search.
30143163
A Fast and Accurate Progressive Algorithm for Training Transductive SVMs
This paper develops a fast and accurate algorithm for training transductive SVMs classifiers, which utilizes the classification information of unlabeled data in a progressive way. For improving the generalization accuracy further, we employ three important criteria to enhance the algorithm, i.e. confidence evaluation, suppression of labeled data, stopping with stabilization. Experimental results on several real world datasets confirm the effectiveness of these criteria and show that the new algorithm can reach to comparable accuracy as several state-of-the-art approaches for training transductive SVMs in much less training time.