• Title/Summary/Keyword: 조건 사후 최대 확률

Search Result 8, Processing Time 0.026 seconds

A Statistical Model-Based Voice Activity Detection Employing the Conditional MAP Criterion with Spectral Deviation (조건 사후 최대 확률과 음성 스펙트럼 변이 조건을 이용한 통계적 모델 기반의 음성 검출기)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
    • /
    • v.30 no.6
    • /
    • pp.324-329
    • /
    • 2011
  • In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the conditional maximum a posteriori (CMAP) with deviation. In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the speech activity decisions and spectral deviation in the pervious frame. Experimental results show that the proposed approach yields better results compared to the CMAP-based VAD using the LR test.

Statistical Model-Based Voice Activity Detection Using the Second-Order Conditional Maximum a Posteriori Criterion with Adapted Threshold (적응형 문턱값을 가지는 2차 조건 사후 최대 확률을 이용한 통계적 모델 기반의 음성 검출기)

  • Kim, Sang-Kyun;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
    • /
    • v.29 no.1
    • /
    • pp.76-81
    • /
    • 2010
  • In this paper, we propose a novel approach to improve the performance of a statistical model-based voice activity detection (VAD) which is based on the second-order conditional maximum a posteriori (CMAP). In our approach, the VAD decision rule is expressed as the geometric mean of likelihood ratios (LRs) based on adapted threshold according to the speech presence probability conditioned on both the current observation and the speech activity decisions in the pervious two frames. Experimental results show that the proposed approach yields better results compared to the statistical model-based and the CMAP-based VAD using the LR test.

Improved Global-Soft Decision Incorporating Second-Order Conditional MAP for Speech Enhancement (음성향상을 위한 2차 조건 사후 최대 확률기법 기반 Global Soft Decision)

  • Kum, Jong-Mo;Chang, Joon-Hyuk
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.34 no.6C
    • /
    • pp.588-592
    • /
    • 2009
  • In this paper, we propose a novel method to improve the performance of the global soft decision which is based on the second-order conditional maximum a posteriori (CMAP). Conventional global soft decision scheme has an disadvantage in that the speech absence probability adjusted by a fixed-parameter was sensitive to the various noise environments. In proposed approach using the second-order CMAP, speech absence probability value is more flexible which exploit not only the current observation but also the speech activity decisions in the previous two frames. Experimental results show that the proposed improved global soft decision method based on second-order conditional MAP yields better results compared to the conventional global soft decision technique with the performance criteria of the ITU-T P. 862 perceptual evaluation of speech quality (PESQ).

Speech Enhancement based on Minima Controlled Recursive Averaging Technique Incorporating Second-order Conditional Maximum a posteriori Criterion (2차 조건 사후 최대 확률 기반 최소값 제어 재귀평균기법을 이용한 음성향상)

  • Kum, Jong-Mo;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
    • /
    • v.46 no.4
    • /
    • pp.132-138
    • /
    • 2009
  • In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the second-order conditional maximum a posteriori (CMAP). From an investigation of the MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the second-order CMAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decisions in the previous two frames. Experimental results show that the proposed MCRA technique based on second-order conditional MAP yields better results compared to the conventional MCRA method.

Speech Enhancement Based on Minima Controlled Recursive Averaging Technique Incorporating Conditional MAP (조건 사후 최대 확률 기반 최소값 제어 재귀평균기법을 이용한 음성향상)

  • Kum, Jong-Mo;Park, Yun-Sik;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
    • /
    • v.27 no.5
    • /
    • pp.256-261
    • /
    • 2008
  • In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the conditional maximum a posteriori criterion. A crucial component of a practical speech enhancement system is the estimation of the noise power spectrum. One state-of-the-art approach is the minima controlled recursive averaging (MCRA) technique. The noise estimate in the MCRA technique is obtained by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. We improve the MCRA using the speech presence probability which is the a posteriori probability conditioned on both the current observation the speech presence or absence of the previous frame. With the performance criteria of the ITU-T P.862 perceptual evaluation of speech quality (PESQ) and subjective evaluation of speech quality, we show that the proposed algorithm yields better results compared to the conventional MCRA-based scheme.

Improving SVM with Second-Order Conditional MAP for Speech/Music Classification (음성/음악 분류 향상을 위한 2차 조건 사후 최대 확률기법 기반 SVM)

  • Lim, Chung-Soo;Chang, Joon-Hyuk
    • Journal of the Institute of Electronics Engineers of Korea SP
    • /
    • v.48 no.5
    • /
    • pp.102-108
    • /
    • 2011
  • Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

Boundary Detection using Adaptive Bayesian Approach to Image Segmentation (적응적 베이즈 영상분할을 이용한 경계추출)

  • Kim Kee Tae;Choi Yoon Su;Kim Gi Hong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
    • /
    • v.22 no.3
    • /
    • pp.303-309
    • /
    • 2004
  • In this paper, an adaptive Bayesian approach to image segmentation was developed for boundary detection. Both image intensities and texture information were used for obtaining better quality of the image segmentation by using the C programming language. Fuzzy c-mean clustering was applied fer the conditional probability density function, and Gibbs random field model was used for the prior probability density function. To simply test the algorithm, a synthetic image (256$\times$256) with a set of low gray values (50, 100, 150 and 200) was created and normalized between 0 and 1 n double precision. Results have been presented that demonstrate the effectiveness of the algorithm in segmenting the synthetic image, resulting in more than 99% accuracy when noise characteristics are correctly modeled. The algorithm was applied to the Antarctic mosaic that was generated using 1963 Declassified Intelligence Satellite Photographs. The accuracy of the resulting vector map was estimated about 300-m.

A Study on the Factors Affecting Examinee Classification Accuracy under DINA Model : Focused on Examinee Classification Methods (DINA 모형에서 응시생 분류 정확성에 영향을 미치는 요인 탐구 : 응시생 분류방법을 중심으로)

  • Kim, Ji-Hyo
    • Journal of the Korea Academia-Industrial cooperation Society
    • /
    • v.14 no.8
    • /
    • pp.3748-3759
    • /
    • 2013
  • The purpose of this study was to examine the classification accuracies of ML, MAP, and EAP methods under DINA model. For this purpose, this study examined the classification accuracies of the classification methods under the various conditions: the number of attributes, the ability distribution of examinees, and test length. To accomplish this purpose, this study used a simulation method. For the simulation study, data was simulated under the various simulation conditions including the number of attributes (K= 5, 7), the ability distribution of examinees (high, middle, low), and test length (J= 15, 30, 45). Additionally, the percent of agreements between true skill patterns(true ${\alpha}$) and skill patterns estimated by the ML, MAP, and EAP methods were calculated. The summary of the main results of this study is as follows: First, When the number of attributes was 5 and 7, the EAP method showed relatively higher average in the percent of exact agreement than the ML and MAP methods. Second, under the same conditions, as the number of attributes increased, the average percent of exact agreement decreased in ML, MAP, and EAP methods. Third, when the prior distribution of examinees ability was different from low to high under the conditions of the same test length, the EAP method showed relatively higher average in the percent of exact agreement than those of the ML and MAP methods. Fourth, the average percent of exact agreement increased in all methods, ML, MAP, and EAP when the test length increased from 15 to 30 and 45 under the conditions of the same the ability distribution of examinees.