由於現在人們的生活非常忙碌，如果要花時間看完一個完整的電視節目(新聞、運動比賽、討論性節目)幾乎是難上加難，所以為了不讓時間浪費在一些無謂的鏡頭或是不感興趣的片斷，例如：暫停、廣告、中場休息 ... 等等。發展一套系統能夠自動辨識出觀眾想看的片斷，變成了一個很重要的研究議題，如何有效的解決這類的問題，也已經引起很多人的關注。
本論文利用HMM的特性來辨識運動影片中的聲音事件，利用：MFCC、頻譜中心(Spectrum Centroid )、頻譜離散度(Spectrum Spread)和頻譜滑動(Spectral Rolloff)，作為描述聲音事件的特徵參數。
在實驗部分我們設計了兩大類的方法：
1. 取得最適合每個事件(Event)的狀態數(State)。
2. 選出最合適的特徵描述所有的聲音事件。
最後我們會比較調整前後辨識率進步的程度，證明我們的方法可以套用在其他的運動比賽中。

Now, It is difficult to watch a complete TV program for people. So, how to extract the most important or interesting fragments become a popular topic for researchers.
In this paper, we used the characteristic of HMM to detect the audio events for the sports videos. We also used MFCC, Spectrum Centroid, Spectrum , Spectral Rolloff Spread to regard as the features of the audio events.
We designed two methods in the experiment:
1. We try to find the best number of states for describing the audio events.
2. We try to find the best rang of Triangular band pass filters for each audio event.
Finally, we compared the results for proving our method can be used in other sports videos.

[1]
Xiong, Z., Radhakrishnan, R., Divakaran, A., Huang, T.S. “Audio Events Detection Based Highlights Extraction from Baseball, Golf and Soccer Games in a Unified Framework ", University of Illinois at Urbana-Champaign,2003
[2]
S.-C. Chen,M.-L. Shyu,C. Zhang,L. Luo, “Detection Of Soccer Goal Shots Using Joint Multimedia Features And Classification Rules” Florida International University, University of Miami,2003
[3]
Z. Xiong, R.Radhakrishnan, A. Divakaran, T. Huang, “Comparing MFCC and MPEG-7 Audio Features for Feature Extraction, Maximum Likelihood HMM and Entropic Prior HMM for Sports Audio Classification” , December 2003
[4]
Jinjun Wang, Changsheng Xu, Chng Eng Siong, Qi Tian” Sports Highlight Detection from Keyword Sequences Using HMM”, School of Computer Engineering, Nanyang Technological University, 2004
[5]
Hyoung-Gook Kim, Steffen Roeber, Amjad Samour, Thomas Sikora, Technische Univ. Berlin,” Detection of goal event in soccer videos”, Technical University of Berlin,2005
[6]
Christian Dittmar,” DRUM DETECTION FROM POLYPHONIC AUDIO VIA DETAILED ANALYSIS OF THE TIME FREQUENCY DOMAIN”,2005
[7]
Jun-Jin Huang, Jhing-Fa Wang,” Spoken Sentence Retrieval Based on MPEG-7 Audio Low-Level Descriptors”, Department of Electrical Engineering National Cheng Kung University, Taiwan.July 21 2003