• Title, Summary, Keyword: VQ

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Speech Recognition Based on VQ/NN using Fuzzy (Fuzzy를 이용한 VQ/NN에 기초를 둔 음성 인식)

  • Ann, Tae-Ock
    • The Journal of the Acoustical Society of Korea
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    • v.15 no.6
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    • pp.5-11
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    • 1996
  • This paper is the study for recognizing single vowels of speaker-independent, and we suppose a method of speech recognition using VQ(Vector Quantization)/NN(Neural Network). This method makes a VQ codebook, which is used for obtaining the observation sequence, and then claculates the probability value by comparing each codeword with the data, finally uses these probability values for the input value of the neural network. Korean signle vowels are selected for our recognition experiment, and ten male speakers pronounced eight single vowels ten times. We compare the performance of our method with those of fuzzy VQ/HMM and conventional VQ/NN According to the experiment result, the recognition rate by VQ/NN is 92.3%, by VQ/HMM using fuzzy is 93.8% and by VQ/NN using fuzzy is 95.7%. Therefore, it is shown that recognition rate of speech recognition by fuzzy VQ/NN is better than those of fuzzy VQ/HMM and conventional VQ/HMM because of its excellent learning ability.

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Text-independent Speaker Identification by Bagging VQ Classifier

  • Kyung, Youn-Jeong;Park, Bong-Dae;Lee, Hwang-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.2E
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    • pp.17-24
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    • 2001
  • In this paper, we propose the bootstrap and aggregating (bagging) vector quantization (VQ) classifier to improve the performance of the text-independent speaker recognition system. This method generates multiple training data sets by resampling the original training data set, constructs the corresponding VQ classifiers, and then integrates the multiple VQ classifiers into a single classifier by voting. The bagging method has been proven to greatly improve the performance of unstable classifiers. Through two different experiments, this paper shows that the VQ classifier is unstable. In one of these experiments, the bias and variance of a VQ classifier are computed with a waveform database. The variance of the VQ classifier is compared with that of the classification and regression tree (CART) classifier[1]. The variance of the VQ classifier is shown to be as large as that of the CART classifier. The other experiment involves speaker recognition. The speaker recognition rates vary significantly by the minor changes in the training data set. The speaker recognition experiments involving a closed set, text-independent and speaker identification are performed with the TIMIT database to compare the performance of the bagging VQ classifier with that of the conventional VQ classifier. The bagging VQ classifier yields improved performance over the conventional VQ classifier. It also outperforms the conventional VQ classifier in small training data set problems.

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Online VQ Codebook Generation using a Triangle Inequality (삼각 부등식을 이용한 온라인 VQ 코드북 생성 방법)

  • Lee, Hyunjin
    • Journal of Digital Contents Society
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    • v.16 no.3
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    • pp.373-379
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    • 2015
  • In this paper, we propose an online VQ Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine. Without degrading the performance of the batch VQ Codebook to the existing data, it was able to take advantage of the newly added data by using a triangle inequality which modifying the VQ Codebook progressively show a high degree of accuracy and speed. The result of applying to test data showed that the performance is similar to the batch method.

Improvement of the TCX Module in AMR-WB+ Codec Using Pyramid VQ (Pyramid VQ를 이용한 AMR-WB+ 코덱 내 TCX 모듈의 성능 개선)

  • Park, Sang-Kuk;Park, Jung-Eun;Baik, Seung-Kweon;Seo, Jung-Il;Kang, Sang-Won
    • The Journal of the Acoustical Society of Korea
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    • v.26 no.3
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    • pp.109-114
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    • 2007
  • In this paper, we Propose a pyramid VQ to quantize the transform coefficients of TCX module for the audio improvement of AMR-WB+ codec. The Proposed pyramid VQ is compared to the $RE_8$ Lattice VQ used in the AMR-WB+ standard codec. demonstrating improvement 4% and 5.7%. respectively, in Mean Squared Error (MSE) and 3.3% and 4.7%. respectively, in Perceptual Evaluation of Audio Quality (PEAQ) by 8-dimensional and 16-dimensional Pyramid VQ.

High Bit Rate Image Coder Using DPCM based on Sample-Adaptive Product Quantizer (표본 적응 프러덕트 양자기에 기초한 DPCM을 이용한 고 전송률 영상 압축)

  • 김동식;이상욱
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.12B
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    • pp.2382-2390
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    • 1999
  • In this paper, we employed a new quantization scheme called sample-adaptive product quantizer (SAPQ) to quantize image data based on the differential pulse code modulation (DPCM) coder, which has fixed length outputs and high bit rates. In order to improve the performance of traditional DPCM coders, the scalar quantizer should be replaced by the vector quantizer (VQ). As the bit rate increases, it will be nearly impossible to implement a conventional VQ or modified VQ, such as the tree-structured VQ, even if the modified VQ can significantly reduce the encoding complexity. SAPQ has a form of the feed-forward adaptive scalar quantizer having a short adaptation period. However, since SAPQ is a structurally constrained VQ, SAPQ can achieve VQ-level performance with a low encoding complexity. Since SAPQ has a scalar quantizer structure, by using the traditional scalar value predictors, we can easily apply SAPQ to DPCM coders. For synthetic data and real images, by employing SAPQ as the quantizer part of DPCM coders, we obtained a 2~3 dB improvement over the DPCM coders, which are based on the Lloyd-Max scalar quantizers, for data rates above 4 b/point.

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Sample-Adaptive Product Quantization and Design Algorithm (표본 적응 프러덕트 양자화와 설계 알고리즘)

  • 김동식;박섭형
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.12B
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    • pp.2391-2400
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    • 1999
  • Vector quantizer (VQ) is an efficient data compression technique for low bit rate applications. However, the major disadvantage of VQ is its encoding complexity which increases dramatically as the vector dimension and bit rate increase. Even though one can use a modified VQ to reduce the encoding complexity, it is nearly impossible to implement such a VQ at a high bit rate or for a large vector dimension because of the enormously large memory requirement for the codebook and the very large training sequence (TS) size. To overcome this difficulty, in this paper we propose a novel structurally constrained VQ for the high bit rate and the large vector dimension cases in order to obtain VQ-level performance. Furthermore, this VQ can be extended to the low bit rate applications. The proposed quantization scheme has a form of feed-forward adaptive quantizer with a short adaptation period. Hence, we call this quantization scheme sample-adaptive product quantizer (SAPQ). SAPQ can provide a 2 ~3dB improvement over the Lloyd-Max scalar quantizers.

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High Bit-Rates Quantization of the First-Order Markov Process Based on a Codebook-Constrained Sample-Adaptive Product Quantizers (부호책 제한을 가지는 표본 적응 프로덕트 양자기를 이용한 1차 마르코프 과정의 고 전송률 양자화)

  • Kim, Dong-Sik
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.49 no.1
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    • pp.19-30
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    • 2012
  • For digital data compression, the quantization is the main part of the lossy source coding. In order to improve the performance of quantization, the vector quantizer(VQ) can be employed. The encoding complexity, however, exponentially increases as the vector dimension or bit rate gets large. Much research has been conducted to alleviate such problems of VQ. Especially for high bit rates, a constrained VQ, which is called the sample-adaptive product quantizer(SAPQ), has been proposed for reducing the hugh encoding complexity of regular VQs. SAPQ has very similar structure as to the product VQ(PQ). However, the quantizer performance can be better than the PQ case. Further, the encoding complexity and the memory requirement for the codebooks are lower than the regular full-search VQ case. Among SAPQs, 1-SAPQ has a simple quantizer structure, where each product codebook is symmetric with respect to the diagonal line in the underlying vector space. It is known that 1-SAPQ shows a good performance for i.i.d. sources. In this paper, a study on designing 1-SAPQ for the first-order Markov process. For an efficient design of 1-SAPQ, an algorithm for the initial codebook is proposed, and through the numerical analysis it is shown that 1-SAPQ shows better quantizer distortion than the VQ case, of which encoding complexity is similar to that of 1-SAPQ, and shows distortions, which are close to that of the DPCM(differential pulse coded modulation) scheme with the Lloyd-Max quantizer.

Image Compression with Edge Directions based on DCT-VQ (DCT-VQ를 기반으로 한 에지의 방향성을 갖는 영상압축)

  • 김진태;김동욱;임한규
    • Journal of Korea Multimedia Society
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    • v.1 no.2
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    • pp.194-203
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    • 1998
  • In this paper, a new DCT-VQ method is proposed which can solve the problems of VQ such as the degradation of edge and enormous calculations. VQ is carried in DCT domain but spatial domain in order to protect the degradation of edge. DCT makes high correlated image data decorrelated and the energy concentrated on a few coefficients. In DCT domain, the DC coefficient is quantized with 8 bits uniform scalar quantizer and the AC coefficients are divided to three regions and coded with vector qiantizer for considering edge components. For the decrease of the calculation and memory, the vectors for three region have small dimension of $1{\times}7$ and use the same codebook. Thus, the proposed method can fully express the edge components by considering AC coefficients in DCT domain and decrease the calculation and memory be reducing the dimension of vectors.

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The bootstrap VQ model for automatic speaker recognition system (VQ 방식의 화자인식 시스템 성능 향상을 위한 부쓰트랩 방식 적용)

  • Kyung YounJeong;Lee Jin-Ick;Lee Hwang-Soo
    • Proceedings of the Acoustical Society of Korea Conference
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    • pp.39-42
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    • 2000
  • A bootstrap and aggregating (bagging) vector quantization (VQ) classifier is proposed for speaker recognition. This method obtains multiple training data sets by resampling the original training data set, and then integrates the corresponding multiple classifiers into a single classifier. Experiments involving a closed set, text-independent and speaker identification system are carried out using the TIMIT database. The proposed bagging VQ classifier shows considerably improved performance over the conventional VQ classifier.

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Fast Codebook Search for Vector Quantization in Image Coding (영상 부호화를 위한 벡터 양자화기에서의 고속 탐색 기법)

  • 고종석;김재균
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.13 no.4
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    • pp.302-308
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    • 1988
  • The paper describes a very simple algorithm for reducing the encoding complexity of vector quantization(VQ), exploiting the feature of a vector currently being encoded. A proposed VQ of 16(=4x4) vector dimension shows a slight performance degradation of about 0.1-1.9dB, however, with only 16-32 among 256 codeword searches, i.e., with just 1/16-1/8 search complexity compared to a full-search VQ. And the proposed VQ scheme is also compared to outperform tree-search VQ with regard to their SNR performance and memory requirement.

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