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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道 | |
| dc.contributor.author | Tay-Ning Hsu | en |
| dc.contributor.author | 許泰寧 | zh_TW |
| dc.date.accessioned | 2021-06-13T02:41:38Z | - |
| dc.date.available | 2010-01-05 | |
| dc.date.copyright | 2007-01-05 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-12-18 | |
| dc.identifier.citation | [1] Takahiro Tanaka, “Karaoke Scoring Apparatus Analyzing Singing Voice Relative To Melody Data,” United States Patent, Mar. 30,1999. Patent No 5889224.
[2] Takuro Sone, Kanehisa Tsurumi, Hirokazu Kato, Takahiro Tanaka, “Karaoke Apparatus With Individual Scoring Of Duet Singers,” United States Patent, Sep. 8, 1998. Patent No 5804752. [3] Tom Jen Tsai, Kanehisa Tsurumi, Satoshi Tachibana, “Karaoke Apparatus,” United States Patent, Mar. 5, 2002. Patent No 6352432 B1. [4] Hung-Min Wang, “Scoring Device and Method for A Karaoke System,” United States Patent, Dec. 4, 2001. Patent NO 6326536 B1. [5] Jae-Gyoo Hong,Ul-Je Kim,“Performance Evaluator for Use in a Karaoke Apparatus,” United States Patent, Sep. 17, 1996. Patent No 5557056. [6] Basavaraj Pawate,“Method and System for Karaoke Scoring,” United States Patent, Feb. 17, 1998. Patent No 5719344. [7] Lorin V. Grubb, Roger B. Dannenberg, “System and Method for Stochastic Score Following,” United States Patent, Jun. 15,1999. Patent No 5913259. [8] John R. Deller,John H. L. Hansen, John G. Proakis,“Discrete-Time Processing of Speech Signals,” Upper Saddle River, NJ: Prentice Hall, 1993. [9] 林昇甫,洪成安, “神經網路入門與圖樣辨識”,台灣, 1993. [10] J.-S. Roger Jang, Ming-Yang Gao, “A Query-by-Singing System Based on Dynamic Programming,” International Workshop on Intelligent Systems Resolutions (the 8th Bellman Continuum), pp. 85-89, Hsinchu, Taiwan, Dec. 2000. [11] B.-K. Yi, C. Faloutsos, “Fast Time Sequence Indexing for Arbitrary Lp Norms,” in Proc. of VLDB, Sept., 2000. [12]F. K.-P. Chan, A. W.-C. Fu, C. Yu, “Haar Wavelets for Efficient Similarity Search of Time-Series: with and without Time Warping,” IEEE Trans. on Knowledge and Data Engineering, 15(3):686-705, May/June, 2003. [13] Asif Ghias, Jonathan Logan, David Chamberlin, and Brian C. Smith, “Query By Humming-musical Information Retrieval In An Audio Database,”ACM Multimedia ‘95, San Francisco, 1995. (http://www2.cs.cornell.edu/zeno/Papers/humming/humming.html) [14] X. Huang, A. Acero, H.-W. Hon, Spoken Language Processing: A Guide to Theory, Algorithm, and System Development: Upper Saddle River,NJ:Prentice Hall 2001. [15] S.-W. Kim, S. Park, W. W. Chu, “An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Database,” in Proc. of IEEE Data Engineering, Germany, pp. 607-614, April, 2001. [16] M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, E. Keogh, “Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measure,” in Proc. of ACM SIGKDD, Aug., 2003. [17] Lawrence Rabiner, B. H. Juang, Fundamentals of Speech Recognition, Upper Saddle River, NJ: Prentice Hall, 1993. [18] F. Korn, H. V. Jagadish, C. Faloutsos, “Efficiently Supporting AD Hoc Queries in Large Datasets of Time Sequences,” in Proc. of ACM SIGMOD, Arizona, pp. 289-300, May, 1997. [19] Christopher Raphael, “Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, pp. 360-370. [20] Christopher Raphael, “Automated Rhythm Transcription,” in Proc. of International Symposium on Music Information Retrieval (IS-MIR 2001), 2001. [21] Adriane Swalm Durey, Mark A. Clements, “Melody Spoting Using Hidden Markov Models,” in Proc. Of International Symposium on Music Information Retrieval (IS-MIR 2001), 2001, pp.109-117. [22] Adriane Swalm Durey, Mark A. Clements, “Features for Spotting Using Hidden Markov Models,” in Proc. of ICASSP 2002, 2002. [23] Steven Young, The HTK Book version 3, Redmond, WA: Microsoft, 2000. [24] Rodger J. McNab, Lloyd A. Smith, Ian H. Witten, Clare L. Henderson, Sally Jo Cunningham,“Towards the Digital Music Library: Tune Retrieval from Acoustic Input,”In Proc. of ACM Digital Libraries Conference, 1996. [25] Rodger J. McNab, Lloyd A. Smith, Ian H. Witten,“Signal Processing for Melody Transcription,” In Proc. of the 19th Australasian Computer Science Conference, 1996. [26] L. Torres, J. Huguet,“An Improvement on Codebook Search for Vector Quantization,” IEEE Transactions on Communication, Vol. 42, No. 2/3/4, pp. 208-210, February/March/April, 1994. [27] Chok-ki Chan, Chi-Kit Ma,“A Fast Method of Designing Better Codebooks for Image Vector Quantization,” IEEE Transactions on Communications, Vol. 42, No. 2/3/4, pp. 237/242, February/March/April, 1994. [28]Peter N. Yianilos,“Data Structures and Algorithms for Nearest Neighbor Search In General Metric Spaces,” In Proc. of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 311-321, Austin, Texas, 25-27 January 1993. [29] B. Chen and J.-S. Roger Jang, “Query by Singing,”in Proc. 11th IPPR Conference on Computer Vision, Graphics, and Image Processing, pp. 529-536, Taiwan, Aug 1998. [30] Rainer Typke, Marc den Hoed, Justin de Nooijer, Frans Wiering, Remco C. Veltkamp, “A Ground Truth For Half A Million Musical Incipits,” in Proc. of DIR 05, January 10-11, 2005. [31] Simon Sheu, Jinxiong Shen, “Effective Filtering for Nearest-Neighbors Queries in Large Time-Series Databases,”in Proc. of the 2003 National Computer Symposium (NCS), Taichung, Taiwan, Dec. 2003, pp. 48-55. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/31302 | - |
| dc.description.abstract | 本研究旨在以類神經網路訓練技術模擬人工歌唱評審程序作為卡拉OK系統評分技術，發展目前流行之卡拉OK設備評分系統之不足。
卡拉OK評分系統中，最直接的就是對評分方式的取決，本文將傳統評分方式由音量與音軌是否符合原唱者的差異性來評分，改由類神經訓練與模擬人工評審評分程序，增加歌唱者原始之聲音歌唱特性，使得原有之評分系統更具客觀與公正性。 實驗之環境完全比照目前卡拉OK歌唱之環境，類神經訓練之樣本完全由此實務環境擷取出來，故實驗之結果更具有參考價值 | zh_TW |
| dc.description.abstract | The purpose of this thesis is to develop a better score system for the Karaoke singers from the technology of ‘Neural Network Training’ and the simulation of the judging process of human juries.
Several ways of scoring are developed under different Karaoke score system. The traditional way of score system is based on comparing the volumes and the tracks of the original singers with those of the Karaoke singers. The problems of lacking objectivity and fair have been identified. In order to rectify these problems, this thesis adopts the alternative approach. This newer and better way of scoring is based on the technology of neural network training and the simulation of the judging process from human juries and is able to consider the singing and vocal characteristics of the original singers in a better way. The samples of the neutral network training are all gotten under the practical environment of Karaoke singing. Thus, these experiments are considered as the valuable contributions of this thesis. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T02:41:38Z (GMT). No. of bitstreams: 1 ntu-95-P90922006-1.pdf: 1138398 bytes, checksum: 42cc0a8c31e5577d4d07bce5fb1774b5 (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | 第一章 導論 1
1.1 前言 1 1.2 現有卡拉OK 評分系統架構簡介 2 1.3 本文提出之智慧型卡拉OK評分系統架構 8 1.4 論文架構 11 2.1 歌曲特徵之擷取方法 12 2.1.1 音量強度曲線 12 2.1.2 基頻軌跡 15 2.2 類神經網路簡介 20 2.3 本研究之實驗參數對應 24 第三章 評分計算系統 27 3.1 前言 27 3.2 神經網路訓練系統之架構與計算流程 27 3.3 自動評分系統之架構 29 3.4 卡拉OK訓練系統各個運算單元之運算流程 30 第四章 實驗架構 32 4.1 評分系統架構與其他系統整合概述 32 4.2 整合程序 34 4.2.1 硬體概述： 34 4.2.2 操作系統： 34 4.2.3 應用程式—點歌系統： 37 第五章 實驗結果 54 5.1 音量之特徵擷取分析實驗： 54 5.2 音調之特徵擷取分析實驗： 63 5.2 類神經網路音量評分訓練與計算 71 5.3 類神經網路音調評分訓練與計算 74 第六章 結論 78 參考資料 79 圖示索引與說明 83 | |
| dc.language.iso | zh-TW | |
| dc.subject | 音樂評分 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | Karaoke | en |
| dc.subject | Neural Network | en |
| dc.title | 數位音頻分析系統研究-以應用於KTV評分 | zh_TW |
| dc.title | Neural Scoring in Karaoke | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳國瑞,黃穎聰 | |
| dc.subject.keyword | 音樂評分,類神經網路, | zh_TW |
| dc.subject.keyword | Neural Network,Karaoke, | en |
| dc.relation.page | 85 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-12-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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