研究生: |
杜政諺 Du, Cheng-Yan |
---|---|
論文名稱: |
藉由擊球音訊特徵進行桌球技術辨識 Skill Recognition Using Acoustic-Characteristics for Table Tennis Sports |
指導教授: |
黃柏鈞
Huang, Po-Chiun |
口試委員: |
吳誠文
Wu, Cheng-Wen 馬席彬 Ma, Hsi-Pin 劉強 Liu, Chiang |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
論文出版年: | 2021 |
畢業學年度: | 109 |
語文別: | 中文 |
論文頁數: | 62 |
中文關鍵詞: | 音訊處理 、特徵工程 、機器學習 、桌球技術辨識 |
外文關鍵詞: | Skill Recognition |
相關次數: | 點閱:1 下載:0 |
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客觀的擊球技術動作類型辨識,在桌球領域幾乎是所有業餘愛好者們共通的需求,但是這項任務往往需要經驗豐富的教練來完成。為了解決此需求,眾多結合影像、感測器的研究與產品陸續被推出,但皆仍因為桌球領域的物理性限制,在成效上有所受限。
在此研究中,我們分析桌球拍擊球產生之聲音訊號,證實透過音訊處理技術,可以從擊球音訊中,推測得知對應之擊球技術類型;也利用聲音在時域上具較高解析度的特性,得以提取到使用攝影機與穿戴式感測器無法獲得之重要特徵,進行較精細的擊球技術類型區分。而在實務應用上,因為擊球的音訊前後必定常常伴隨著球與球桌接觸碰撞的聲音,為了避免這些碰撞聲干擾擊球音訊的分析,所以本研究中亦提出了一套區分擊球音訊與觸桌音訊的運算方式,藉此區分桌球與球桌碰撞聲。
本研究中,我們邀請台灣桌球成人國手選手,輔助建立桌球聲訊資料庫。目前資料庫內之音訊,包含擊球技術之音訊,與擊球觸桌之音訊資料,共1246筆。據本論文所提出,結合物理特性與機器學習之音訊處理演算法,在上述資料庫中,區分擊球音訊與觸桌音訊之演算法,準確率可達98.86%;區分擊球技術為扣殺、攻球、前衝、弧圈與切球五種技術動作,準確率可達90.25%。經量測驗證,此音訊處理演算法,可於1秒內完成高達每秒50個音訊事件以上之聲音訊號分析,說明此演算法具有可Real-time執行之可行性。
此音訊處理演算法系統在未來應用上,除了單獨使用之外,亦可與影像辨識系統或是慣性感測系統整合,架構更完善的系統,於情資蒐集、教學應用、競賽報導等情境,可提供更有效的工具。
Each athlete is eager to understand the efficiency of his skill to achieve higher performance. Conventionally it is achieved by the comments from coaches. Even though, without quantitative values limits the players' perception.
This research focuses on the recognition of table tennis skill using acoustic-characteristics when the ball is hit. Compared with the techniques like video analyses and wearable electronics that are proposed to understand the posture, using acoustics has several advantages. Most importantly, the higher sampling rate and resolution are helpful for understanding the moment of strokes. With such advantage, more detailed table tennis skills can be recognized.
In this research, by combining the physical-based knowledge and the machine learning techniques, we can distinguish the difference between ball-table and ball-paddle impacts, and five skills of Smash, Attack, Speedo, Loop, and Chop. The accuracy reaches 98.86% and 90.25% respectively. The processing time of the proposed system is less than 650ms for 50 events in 1 second, which makes real-time response possible.
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