15124735
Chip design of MFCC extraction for speech recognition
The mel frequency cepstral coefficient (MFCC) is one of the most important features required among various kinds of speech applications. In this paper, the first chip for speech features extraction based on MFCC algorithm is proposed. The chip is implemented as an intellectual property, which is suitable to be adopted in a speech recognition system on a chip. The computational complexity and memory requirement of MFCC algorithm are analyzed in detail and improved greatly. The hybrid table look-up scheme is presented to deal with the elementary function value in the MFCC algorithm. Fixed-point arithmetic is adopted to reduce the cost under the accuracy studies of finite word length effect. Finally, the area-efficient design is implemented successfully into the single Xilinx XC4062XL FPGA.
19723482
A learning process to the identification of feature points on Chinese characters
The paper describes a new stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on the convolution operations with a set of 1-D Gabor templates is efficient in extracting the stroke segments of the character and robust in noise tolerance. Furthermore, unlike conventional feature-point detection techniques where decision rules and thresholds have to be specified, our learning-based technique for feature-point identification implicitly represents the rules and thresholds without further parameter adjustments. Experimental results show that the learning-based technique is capable of generalizing the learning knowledge to identify feature points and can get an average identification rate of 95.27% for hand-printed test characters and 96.78% for machine-printed test characters.
17964420
A learning process to the identification of feature points on Chinese characters
The paper describes a novel stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on convolution operations with a set of one-dimensional (1D) Gabor templates efficiently extracts the stroke segments from noisy and degraded characters. Furthermore, the relationship between endpoints of stroke segments is modeled as junction structure during a learning process. Finally, each endpoint is identified as a feature point to determine the junction structure by the learning-based technique, rather than rule-based techniques with manual rule creation. Experimental results indicate that the learning-based technique can generalize learning knowledge to identify 1200 feature points with an average identification rate of 93.58% for test set, using k-fold cross-validation testing.
11945508
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis
In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate
45865876
Graph theoretic characterization and reliability of the generalized Boolean n-cube network
In this paper, a general class of Boolean n-cube structures is investigated. The interconnection is based on a mixed radix number system which results in a variety of generalized Boolean n-cube structures for a given number of processors N = x·2y, where x and y are positive integers. A number of interesting properties of the network are revealed. By a constructive method, the node connectivity of this network is found. Finally, we show that the graph is super-λ and is an optimal reliable structure for interconnection networks.
21513395
VLSI architecture and implementation for FS1016 CELP decoder with reduced power and memory requirements
Abstract The code-excited linear predictive (CELP) coder is the most effective technique among various linear predictive coding methods for speech compression. Hence, designing a relatively low-cost and low-power CELP decoder chip for the portable systems and wireless digital communication environment becomes increasingly important. This paper presents the VLSI architecture and chip implementation for the FS1016 CELP decoder with reduced power and memory requirements. A single-chip implementation of the CELP decoder drastically reduces the cost and the size of many CELP vocoder systems. The decoder chip can achieve the following: (1) excellent accuracy results due to the accuracy studies for the finite word length, (2) power savings and high-speed operations owing to the combined advantages of pipeline and concurrent processing structures, (3) table size reducing by applying the memoryless realization for stochastic codebook and partial sums technique and (4) specification satisfying the FS1016 CELP coder. Fabricated with 0.8 μm double-metal CMOS technology, the chip contains approximately 13 000 transistors occupying a 6.1 × 6.2 mm2 area. It has been tested to be fully functional at IMS XL-60 tester and is the first working FS1016 CELP 4.8 K decoder chip. In particular, the fixed-point accuracy studies for the CELP decoder are also addressed herein.
12807394
A novel data caching scheme for multimedia servers
Abstract In this paper, we propose a Bi-directional Fragmental Pipelining (BFP) technique and its variable buffer size data-caching scheme BFPV to reduce the disk I/O bandwidth requirement for multimedia servers. Our mathematical analysis shows that the BFP technique is superior to the traditional unidirectional pipelining technique in terms of memory buffer space requirement. We further demonstrate that the memory buffer management using BFPV is better than that of using the fixed buffer size approach BFPF. We have mathematically proved that BFPV saves more disk I/O bandwidth than BFPF does using the same memory buffer space. Our simulation results have quantitatively confirmed our analysis.
34826531
User-assisted query translation for interactive CLIR
We view interactive Cross-Language Information Retrieval (CLIR) as an iterative process in which the searcher and the retrieval system collaborate to find documents that satisfy the searcher’s needs, regardless of the language in which those documents are written. Our motivation is that humans and machines can bring complementary strengths to this process. Machines are excellent at repetitive tasks that are well specified; humans bring creativity and exceptional pattern recognition capabilities. Properly coupling these capabilities can result in a synergy that greatly exceeds the ability of either human or machine alone. Designers of CLIR systems can select from a variety of fully automatic techniques to overcome problems with unknown terms and translation ambiguity, but automatic processing of this sort risks reducing the searcher’s understanding of system operation. This, in turn, tends to work against the synergy that we seek to accomplish. We are therefore exploring more transparent approaches to support interactive CLIR.
492137
Blind source extraction in various ill-conditioned cases
The paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but-singular; the number of sensors is smaller than that of sources; the number of sensors is larger than that of sources, but the column rank of the mixing matrix is deficient; the number of sources is unknown and the column rank of the mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.
30494509
Translating unknown cross-lingual queries in digital libraries using a web-based approach
15400274
Cluster Monte Carlo Algorithms and Their Applications
We introduce the Markov chain Monte Carlo method and review the background of the cluster algorithms in statistical physics. One of the first such successful algorithm was developed by Swendsen and Wang eight years ago. In contrast to the local algorithms, cluster algorithms update dynamical variables in a global fashion. Therefore, large changes are made in a single step. The method is very efficient, especially near the critical point of a second-order phase transition. Studies of various statistical mechanics models and some generalizations of the algorithm will be briefly reviewed. We mention applications in other fields, especially in imaging processing.