Full metadata record
DC Field | Value | Language |
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dc.contributor.author | 陳孟君 | en_US |
dc.contributor.author | Meng-Chun Chen | en_US |
dc.contributor.author | 白明憲 | en_US |
dc.contributor.author | Ming-Sian Bai | en_US |
dc.date.accessioned | 2014-12-12T02:54:02Z | - |
dc.date.available | 2014-12-12T02:54:02Z | - |
dc.date.issued | 2005 | en_US |
dc.identifier.uri | http://140.113.39.130/cdrfb3/record/nctu/#GT009314580 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/78555 | - |
dc.description.abstract | 本論文乃應用聲音訊號分類技術以及獨立成分分析於引擎噪音診斷系統以及音訊前處理系統。 傳統上,機車修護工作仰賴修護人員的經驗來檢視及排除故障,然而修護人員養成不易。隨著現代科技進步,今日引擎之故障檢修皆以電腦診斷方式進行,唯此方式對於因機件異常所產生的噪音訊號無法判斷。本研究旨在以智慧型分類技術來建構一套有效率的引擎故障聲訊辨識系統,以期在一般引擎故障檢修範圍之外,能夠及早發現其它的機件異常現象,並協助維修人員正確辨識故障來源。 隨著網路的發達以及數位音樂的普及,透過網路下載音樂於個人電腦已成為趨勢。然而這些大量下載的音樂需要靠人工的方式分類整理,本論文提出的系統能將這些音樂自動分類,歌曲部分以歌手作區分,演奏曲則以樂器來區分。此技術亦可應用於MP3隨身聽,針對儲存在機身中的所有歌曲,自動將歌曲系統化分類。當我們聆聽歌曲時,透過此系統,隨身聽可以搜尋同歌手的專輯清單,或是同類樂器的樂曲,建立個人的音樂資料庫。 獨立成分分析技術可從混合的資料當中分離出相互之間互為統計獨立的成分。本研究將歌曲的左右聲道分別當作兩個混合聲源,經過獨立成份分析驗證後發現歌曲中音樂成分可被分離,但是無法抽取出人聲的部分。若用此技術來萃取背景噪音中之語音訊號,亦無法去除所有的背景噪音,但可以降低背景噪音,突顯出語音的部分。 | zh_TW |
dc.description.abstract | A processor that integrates various intelligent classification and preprocessing algorithms is presented in this thesis. Classification algorithms including the Nearest Neighbor Rule (NNR), the Artificial Neural Networks (ANN), the Fuzzy Neural Networks (FNN), and the Hidden Markov Models (HMM) are employed to classify and identify engine noise, singers and instruments. Audio features in the time and frequency domains are extracted and preprocessed prior to classification. A training phase is required to establish a feature space template. This is followed by a test phase, where the audio features of the test data are calculated and matched with the feature space template. In addition to audio classification, the proposed system provides several Independent Component Analysis (ICA)-based preprocessing functions for blind source separation, voice removal, and noise reduction. The proposed techniques were applied to process various kinds of audio program materials. The results reveal that the performance of methods are satisfactory, but varies with algorithm and program material used in the tests. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | 分類 | zh_TW |
dc.subject | 引擎噪音診斷 | zh_TW |
dc.subject | 獨立成分分析 | zh_TW |
dc.subject | 類神經網路 | zh_TW |
dc.subject | 模糊類神經網路 | zh_TW |
dc.subject | 隱藏式馬可夫模型 | zh_TW |
dc.subject | Classification | en_US |
dc.subject | Engine Noise Diagnostics | en_US |
dc.subject | Independent Component Analysis | en_US |
dc.subject | Artificial Neural Networks | en_US |
dc.subject | Fuzzy Neural Networks | en_US |
dc.subject | Hidden Markov Models | en_US |
dc.title | 應用聲音訊號分類技術於引擎噪音診斷以及音訊前處理 | zh_TW |
dc.title | Intelligent Classification of Sound Signals with Applications in Engine Noise Diagnostics and Audio Preprocseeing | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | 機械工程學系 | zh_TW |
Appears in Collections: | Thesis |