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You searched for +publisher:"NSYSU" +contributor:("Wei-Bin Liang"). Showing records 1 – 3 of 3 total matches.

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NSYSU

1. Hsieh, Chi-hao. Research on Improving Audio Fingerprinting Extraction Method.

Degree: Master, Computer Science and Engineering, 2015, NSYSU

In this paper, we propose a robust landmark-based audio fingerprinting method, which is used for music information retrieval under noisy environments effectively. To increase the robustness of the audio fingerprinting, we propose to apply a high pass filter in each frame from the spectrogram. Then we examine the horizontal and vertical peaks simultaneously to reduce the number of peak pair. Landmarks are represented by peak pairs using the temporal and the spectral distances between two adjacent peaks. Finally, the landmarks are mapped to hash values to represent audio fingerprints. In the inquiry stage, according to the distribution of audio fingerprinting, we can quickly identify the most likely song information. To evaluate the proposed approach, 10,000 songs were collected and manually added different types and intensities of noises. Experimental results show that our proposed system significantly outperforms a baseline system based on a well-known method. Since the amount of extracted fingerprints is much smaller, the computational cost in the retrieval procedure is much smaller. Furthermore, the performance degradation in the presence of noise is much more graceful. Thus, the proposed method is indeed very robust to noise. Advisors/Committee Members: Wei-Bin Liang (chair), Lo Hung yi (chair), Chia-Ping Chen (committee member).

Subjects/Keywords: landmark; music information retrieval; audio fingerprint; noise robustness; hash key

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hsieh, C. (2015). Research on Improving Audio Fingerprinting Extraction Method. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0725115-141523

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Hsieh, Chi-hao. “Research on Improving Audio Fingerprinting Extraction Method.” 2015. Thesis, NSYSU. Accessed August 08, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0725115-141523.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hsieh, Chi-hao. “Research on Improving Audio Fingerprinting Extraction Method.” 2015. Web. 08 Aug 2020.

Vancouver:

Hsieh C. Research on Improving Audio Fingerprinting Extraction Method. [Internet] [Thesis]. NSYSU; 2015. [cited 2020 Aug 08]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0725115-141523.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hsieh C. Research on Improving Audio Fingerprinting Extraction Method. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0725115-141523

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

2. Wang, Yo-Ping. Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages.

Degree: Master, Computer Science and Engineering, 2013, NSYSU

√£√£According to the traditional methods of noise robustness, the Minimum Mean Square Error(MMSE) feature transformation method was usually used to estimate clean feature. In order to maintain the smoothness and continuity from original feature, we use the method of noise robustness which is based on Gaussian Mixture Model to remove the noise instead of estimating the clean feature. Our method assumed that the lower noisy corpus of parallel corpora is the clean one. We find the mean vector corresponding to the noise by using the trained Gaussian Mixture Model, and use the concept of MMSE to calculate the margin of a noise effect. Finally we estimated the distance between noise feature of parallel corpura by MMSE and subtracted it from the higher noise feature. We use AURURA 3.0 corpus by experiment to estimate noise robustness performance. Test data will be classified by the trained noise classfier, and select the corresponding GMM mapping model, estimated the mean noise vectors under this model and generated the noise feature through a linear combination of unequal weight. Finally, it is easy to remove the noise by subtraction to make noise reduction. Advisors/Committee Members: Wei-Bin Liang (chair), Chia-Ping Chen (committee member), Tsung-hsien Wu (chair).

Subjects/Keywords: GMM; Noise Robustness; MMSE; AURORA 3.0

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, Y. (2013). Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0207113-161555

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Yo-Ping. “Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages.” 2013. Thesis, NSYSU. Accessed August 08, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0207113-161555.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Yo-Ping. “Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages.” 2013. Web. 08 Aug 2020.

Vancouver:

Wang Y. Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages. [Internet] [Thesis]. NSYSU; 2013. [cited 2020 Aug 08]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0207113-161555.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Y. Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages. [Thesis]. NSYSU; 2013. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0207113-161555

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

3. Yeh, Bing-Feng. Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition.

Degree: Master, Computer Science and Engineering, 2012, NSYSU

In this paper, we propose a new method for noise robustness base on Gaussian Mixture Model (GMM), and the method we proposed can estimate the noise feature effectively and reduce noise effect by plain fashion, and we can retain the smoothing and continuity from original feature in this way. Compared to the traditional feature transformation method MMSE(Minimum Mean Square Error) which want to find a clean one, the different is that the method we proposed only need to fine noise feature or the margin of noise effect and subtract the noise to achieve more robustness effect than traditional methods. In the experiment method, the test data pass through the trained noise classifier to judge the noise type and SNR, and according to the result of classifier to choose the corresponding transformation model and generate the noise feature by this model, and then we can use different weight linear combination to generate noise feature, and finally apply simple subtraction to achieve noise reduction. In the experiment, we use AURORA 2.0 corpus to estimate noise robustness performance, and using traditional method can achieve 36:8% relative improvement than default, and the our method can achieve 52:5% relative improvement, and compared to the traditional method our method can attain 24:9% relative improvement. Advisors/Committee Members: Chung-Hsien Wu (chair), Chia-Ping Chen (committee member), Wei-Bin Liang (chair).

Subjects/Keywords: MMSE; GMM; Noise Robustness

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yeh, B. (2012). Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828112-195916

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yeh, Bing-Feng. “Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition.” 2012. Thesis, NSYSU. Accessed August 08, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828112-195916.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yeh, Bing-Feng. “Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition.” 2012. Web. 08 Aug 2020.

Vancouver:

Yeh B. Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition. [Internet] [Thesis]. NSYSU; 2012. [cited 2020 Aug 08]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828112-195916.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yeh B. Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition. [Thesis]. NSYSU; 2012. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0828112-195916

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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