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研究生:蔡峰杰
研究生(外文):Feng-Jie Tsai
論文名稱:從多重音樂資料串流中建立一個具有音樂心情偏好的虛擬音樂頻道
論文名稱(外文):Building a Virtual Music Channel with Preferred Music Mood from Multiple Data Streams
指導教授:陳良弼陳良弼引用關係
指導教授(外文):Arbee L.P. Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學門:電算機學門
學類:系統設計學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:44
中文關鍵詞:線上廣播電台虛擬頻道多重資料串流表演單
外文關鍵詞:online radiovirtual channelmultiple data streamplaybill
相關次數:
  • 被引用被引用:0
  • 點閱點閱:212
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  • 下載下載:45
  • 收藏至我的研究室書目清單書目收藏:0
在本論文中,我們提出了一個名為『虛擬頻道(virtual channel)』的新型態線上廣播電台(online radio)之資訊服務系統。在這個系統中,它最主要的功能是讓我們透過統合與分析各個網路電台的播放內涵後,經由自動選取並轉換到合適的電台的機制,讓我們享受到一個持續撥放著音樂的環境,也由於我們不需要手動地尋找目前正在撥放著有音樂內容的電台(就如同擁有一個一直撥放音樂的電台),所以我們的系統才名為虛擬音樂頻道系統;此外在本系統中,我們亦分析並判斷出每一個音樂頻道所屬的音樂心情(music mood),也就是在已經判斷出目前正在撥放著音樂的電台中,再進一步地判別出此撥放的音樂所帶給人的感覺是被歸類為悲傷、滿足、急躁或是熱鬧的機制;在經過以上兩層的判斷程序後,我們可以根據使用者的習慣或是外在所需的音樂環境(例如在輕鬆時想要有滿足風格的音樂氣氛等)來建立起一個可動態調整的音樂電台選擇清單(playbill)。綜合以上的系統服務需求,我們首先要研究的是:如何有效率的擷取出每一個網路電台資料串流中的特徵值,並且透過有效率的分析與判斷機制來找出正在撥放著含有音樂內涵的網路電台;緊接著是如何透過特徵值的分析來判斷出那些正被撥放的音樂片段們所代表的音樂心情。由於在資料串流的環境下有著反應時間的限制,所以我們必須找出兼具有特徵值組合精簡以及判斷時間迅速的分類機制的來建構此系統。最後我們將展示一系列的實驗來驗證系統中分類機制的判斷正確率以及評估系統整體的表現。
In this thesis, an online radio service called virtual channel is proposed. This service provides a continuous musical environment where the playbill is made according to the atmosphere or the user’s habit. To achieve this goal, we first extract and analyze the audio features of multiple data streams from the online radio servers. Through this step, we classify all radio channels and determine whether the content contains musical intension. After that, we continue to analyze the remaining channels which are playing music and detect the musical mood of the music slices by music psychological theory and heuristic rules. The playbill according to the user’s preference can then be arranged. Due to the constraint of the processing time in the streaming environment, we have to process all schemes mentioned above in real time. Therefore, we have to shorten the judging time by extracting few features and using efficient classifiers to satisfy the limitation. Finally, we perform a series of experiments to evaluate the performance of the proposed framework, and the discrimination rate of the schemes. The results show that the mechanism of virtual channel is workable in the streaming environment.
Abstract III
Acknowledgements IIII
Contents IIV
List of Figures V
List of Tables VII
List of Algorithms VIII
1. Introduction 1
1.1 Motivation 1
1.2 Related Work 2
1.3 System Design 4
2. System Overview 6
2.1 Preliminary 6
2.2 System Framework 8
3. Algorithms and Example of system Processing 12
3.1 Algorithms 12
3.2 Processing Example 15
4. Feature Extraction and Analysis 25
4.1 Finding the songs with preferred Music Mood 29
4.1.1 Music Intension Detection 29
4.1.2 Music Mood Classification 31
4.2 Tuning point Detection 34
5. Experiment results 36
5.1 Experimental Environment 37
5.2 Evaluations 38
6. Conclusion 41
7. Reference 42
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