• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.673-680
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• 2003

In this paper, we study the enhancement of VQ (Vector Quantization) design for text independent speaker recognition. In a concrete way, we present a non-iterative method which makes a vector quantization codebook and this method performs non-iterative learning so that the computational complexity is epochally reduced The proposed Classified Space VQ (CSVQ) design method for text Independent speaker recognition is generalized from Semi-noniterative VQ design method for text dependent speaker recognition. CSVQ contrasts with the existing desiEn method which uses the iterative learninE algorithm for every traininE speaker. The characteristics of a CSVQ design is as follows. First, the proposed method performs the non-iterative learning by using a Classified Space Codebook. Second, a quantization region of each speaker is equivalent for the quantization region of a Classified Space Codebook. And the quantization point of each speaker is the optimal point for the statistical distribution of each speaker in a quantization region of a Classified Space Codebook. Third, Classified Space Codebook (CSC) is constructed through Sample Vector Formation Method (CSVQ1, 2) and Hyper-Lattice Formation Method (CSVQ 3). In the numerical experiment, we use the 12th met-cepstrum feature vectors of 10 speakers and compare it with the existing method, changing the codebook size from 16 to 128 for each Classified Space Codebook. The recognition rate of the proposed method is 100% for CSVQ1, 2. It is equal to the recognition rate of the existing method. Therefore the proposed CSVQ design method is, reducing computational complexity and maintaining the recognition rate, new alternative proposal and CSVQ with CSC can be applied to a general purpose recognition.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.67-75
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• 2012

Optimal quantizers in conducting the entropy-constrained quantization for high bit rates have the lattice structure. The quantization process is simple due to the regular structure, and various quantization algorithms are proposed depending on the lattice. Such a lattice vector quantizer (VQ) can be implemented by using the sample-adaptive product quantizer (SAPQ) and its output can also be easily entropy encoded. In this paper, the entropy encoding scheme for the lattice VQ is proposed based on SAPQ, and the performance of the proposed lattice VQ, which is based on SAPQ with the entropy coder, is asymptotically compared as the rate increases. It is shown by experiment that the gain for the memoryless Gaussian source also approaches the theoretic gain for the uniform density case.

• The Journal of the Acoustical Society of Korea
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• v.17 no.8
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• pp.68-72
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• 1998

본 논문에서는 기존의 DPCM에 의한 압축방법보다 더 낮은 비트율을 갖는 압축방 법을 제안한다. 각 화소의 예측오차 값은 DPCM방법에 의해 양자화되고, 양자화된 예측오차 의 열은 예측오차의 학습된 열로 구성된 코드북과 비교된다. 비교과정은 벡터양자화 방법과 동일하고, 그 결과 코드북의 주소를 생성한다. 제안된 방법은 DPCM과 동일한 복원 영상의 화질을 보이지만, 더 낮은 비트율을 얻을 수 있다.

• The Journal of the Acoustical Society of Korea
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• v.31 no.2
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• pp.93-99
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• 2012

For speech coding, a vocal tract is modeled using Linear Predictive Coding (LPC) coefficients. The LPC coefficients are typically transformed to Line Spectral Frequency (LSF) parameters which are advantageous for linear interpolation and quantization. If multidimensional LSF data are quantized directly using Vector-Quantization (VQ), high rate-distortion performance can be obtained by fully utilizing intra-frame correlation. In practice, since this direct VQ system cannot be used due to high computational complexity and memory requirement, Split VQ (SVQ) is used where a multidimensional vector is split into multilple sub-vectors for quantization. The LSF parameters also have high inter-frame correlation, and thus Predictive SVQ (PSVQ) is utilized. PSVQ provides better rate-distortion performance than SVQ. In this paper, to implement the optimal predictors in PSVQ for voice storage devices, we propose Multi-Frame AR-model based SVQ (MF-AR-SVQ) that considers the inter-frame correlations with multiple previous frames. Compared with conventional PSVQ, the proposed MF-AR-SVQ provides 1 bit gain in terms of spectral distortion without significant increase in complexity and memory requirement.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.101-112
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• 1999

In this paper, the codebook design method of VQ (vector quantization) is proposed an method to extract feature data of image for multimedia information searching. Conventional VQ codebook design methods are unsuitable to extract the feature data of images because they have too much computation time, memory for vector decoding and blocking effects like DCT (discrete cosine transform). The proposed design method is consists of the feature extraction by WT (wavelet transform) and the data group divide method by PCA (principal component analysis). WT is introduced to remove the blocking effect of an image with high compressing ratio. Computer simulations show that the proposed method has the better performance in processing speed than the VQ design method using SOM (self-organizing map).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.6B
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• pp.810-819
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• 2001

칼라가 물체 인식에 아주 효율적인 단서를 제공하지만 칼라 분포는 시청 조건과 카메라의 위치에 아주 큰 영향을 받는다. 생김새와 모양의 변화에 의한 칼라 분포 변화 문제를 해결하기 위해 본 논문에서는 밝기 값의 변화에 영향을 받지 않고, 색상(hue) 성분에 민감한 칼라 벡터각(color vector angle)을 이용하여 칼라 에지를 추출한 후, 영상의 화소들을 평탄 화소와 에지 화소로 구분하여 칼라 특징 값을 추출하였다. 에지 화소의 경우에는 에지 주위 칼라 쌍의 전체 분포를 HLS 색좌표계의 비균일 양자화를 통해 칼라 인접 히스토그램(color adjacency histogram)으로 표현하고, 평탄 화소의 경우에는 HLS 색좌표계의 비균일 양자화와 칼라 벡터각 균일 양자화를 통해 칼라 벡터각 히스토그램(color vector angle histogram)을 구성하여 공간적인 칼라분포를 표현하였다. 제안한 칼라 히스토그램을 이용하여 영상 검색에 적용하여 성능을 실험한 결과, 작은 빈의 수를 가지는 제안한 방법이 기존의 방법들보다 훨씬 효율적이고, 생김새와 모양의 변화에 아주 강건한 영상 검색이 가능하였고, 기존의 칼라 히스토그램 역투사 방법보다 훨씬 정확한 물체 위치 추정이 가능함을 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.47-54
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• 2001

In this paper, we present a variable dimension vector quantization for spectral magnitudes. Espectially, spectral magnitudes of the Harmonic coder, need variable dimension quantizer because those are not fixed dimension. So, this paper present efficient quantization methods. These methods use variable Discrete Cosine Transform(DCT) for spectral magnitude parameters and NSTVQ which is combined odd/even, split and multi-stage structure, proposed quantization methods use Spectral Distortion(SD) for performance measure. Consequently, Multi-Stage Nonsquare Transform Vector Quantization(MSNSTVQ) is the best in performance measure.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.29-34
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• 2001

In this paper, we designed an LSF coefficient quantizer of the wideband speech codec that can produce high quality speech service. For the efficient LSF coefficient quantizer, the interframe correlation was used. Also we separately quantized the LSF coefficients with high and low interframe correlation. Predictive pyramid vector quantizer (PVQ) was used for quantizing the LSF coefficients with high interframe correlation, and PVQ was used for quantizing the LSF coefficients with low interframe correlation. Experiments show that the proposed UF quantizer can quantize LSF information in 40 bits/frame, with an average spectral distortion (SD) of 1 dB and less than 3.87% frames having SD greater than 2 dB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.761-771
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• 1994

This paper focuses on how spectrum information can be represented efficiently in a very low bit rate CELP speech coder. To achieve the goal, an LPC cepstral coefficients VQ scheme representing the spectrum information in a CELP coder is proposed. To represent the spectrum information using LPC cepstrums, three different cepstral distance measures having different spectral meanings in the frequency domain are considered, and their performances are compared and analyzed. The experimental results show that spectrum information in low bit rate CELP coders can be represented very efficiently using the proposed LPC cepstral vector quantization scheme.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.309-315
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• 2012

Active data are the input data which are changed its membership as Vector Quantization codebook generation algorithm is processed. In the process of VQ codebook generation algorithm performed, the actual active data out of the entire input data will be less presented as the process is performed. Therefore, if we can accurately find the active data and only if we are going to do VQ codebook generation on the active data, then we can significantly reduce the overall generation time. In this paper, we presented the triangle inequality based algorithm to select the active data. Experimental results show that our algorithm is superior to other methods in terms of the VQ codebook generation time.