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Codebook

About: Codebook is a research topic. Over the lifetime, 8492 publications have been published within this topic receiving 115995 citations.


Papers
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Proceedings ArticleDOI
26 Apr 1985
TL;DR: A vector quantization (VQ) codebook was used as an efficient means of characterizing the short-time spectral features of a speaker and was used to recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) classification rule.
Abstract: In this study a vector quantization (VQ) codebook was used as an efficient means of characterizing the short-time spectral features of a speaker. A set of such codebooks were then used to recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) classification rule. A series of speaker recognition experiments was performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification accuracy was achieved. The effects, on performance, of different system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions were also studied in detail.

493 citations

Proceedings ArticleDOI
13 Jun 2010
TL;DR: This paper proposes to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features, and incorporates it into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features.
Abstract: Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization process of BoW based image representation. However, in the feature quantization process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness of quantization. In this paper, to alleviate the impact of this problem, we propose a Laplacian sparse coding method, which will exploit the dependence among the local features. Specifically, we propose to use histogram intersection based kNN method to construct a Laplacian matrix, which can well characterize the similarity of local features. In addition, we incorporate this Laplacian matrix into the objective function of sparse coding to preserve the consistence in sparse representation of similar local features. Comprehensive experimental results show that our method achieves or outperforms existing state-of-the-art results, and exhibits excellent performance on Scene 15 data set.

483 citations

Proceedings ArticleDOI
03 Apr 1990
TL;DR: Initial efforts to make Sphinx, a continuous-speech speaker-independent recognition system, robust to changes in the environment are reported, and two novel methods based on additive corrections in the cepstral domain are proposed.
Abstract: Initial efforts to make Sphinx, a continuous-speech speaker-independent recognition system, robust to changes in the environment are reported. To deal with differences in noise level and spectral tilt between close-talking and desk-top microphones, two novel methods based on additive corrections in the cepstral domain are proposed. In the first algorithm, the additive correction depends on the instantaneous SNR of the signal. In the second technique, expectation-maximization techniques are used to best match the cepstral vectors of the input utterances to the ensemble of codebook entries representing a standard acoustical ambience. Use of the algorithms dramatically improves recognition accuracy when the system is tested on a microphone other than the one on which it was trained. >

461 citations

Journal ArticleDOI
W.H. Equitz1
TL;DR: The pairwise nearest neighbor (PNN) algorithm is presented as an alternative to the Linde-Buzo-Gray (1980, LBG) (generalized Lloyd, 1982) algorithm for vector quantization clustering.
Abstract: The pairwise nearest neighbor (PNN) algorithm is presented as an alternative to the Linde-Buzo-Gray (1980, LBG) (generalized Lloyd, 1982) algorithm for vector quantization clustering. The PNN algorithm derives a vector quantization codebook in a diminishingly small fraction of the time previously required, without sacrificing performance. In addition, the time needed to generate a codebook grows only O(N log N) in training set size and is independent of the number of code words desired. Using this method, one can either minimize the number of code words needed subject to a maximum rate. The PNN algorithm can be used with squared error and weighted squared error distortion measure. Simulations on a variety of images encoded at 1/2 b/pixel indicate that PNN codebooks can be developed in roughly 5% of the time required by the LBG algorithm. >

456 citations

Journal ArticleDOI
TL;DR: This work constructs analytically optimal codebooks meeting the Welch lower bound, and develops an efficient numerical search method based on a generalized Lloyd algorithm that leads to considerable improvement on the achieved I/sub max/ over existing alternatives.
Abstract: Consider a codebook containing N unit-norm complex vectors in a K-dimensional space. In a number of applications, the codebook that minimizes the maximal cross-correlation amplitude (I/sub max/) is often desirable. Relying on tools from combinatorial number theory, we construct analytically optimal codebooks meeting, in certain cases, the Welch lower bound. When analytical constructions are not available, we develop an efficient numerical search method based on a generalized Lloyd algorithm, which leads to considerable improvement on the achieved I/sub max/ over existing alternatives. We also derive a composite lower bound on the minimum achievable I/sub max/ that is effective for any codebook size N.

445 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023217
2022495
2021237
2020383
2019432
2018364