本論文探討的內容主要關於人聲分類之研究。在第一個層次，我們將家庭環境中的聲音分為說話聲、人聲非說話聲以及環境音三種。為了達到此目標，我們尋找了許多特徵並找出合適的幾個作為參數。
在夜間睡眠研究的部份，我們希望可以將整晚的睡眠資料分為鼾聲與非鼾聲兩部份，針對鼾聲的部份再深入去探討是否有呼吸中止的情況產生。若受試者是在醫院或者專業睡眠實驗室收錄資料，則會有其他睡眠生理訊號可供分析。本論文試著找出鼾聲與振動、睡姿與睡眠階段的關係並有初步的成果。 / In this thesis, we describe the classification of audio signals in a smart home environment and in all-night sleep studies. In a home environment, our objective is different from most audio scene analysis projects in that we are mainly concerned with the distinction of human and non-human sounds. Toward this goal, we identify appropriate features to be extracted from the audio files and discuss the rationale behind choosing a particular feature.
In all-night sleep recording, we describe the classification of audio signals recorded in all-night sleep studies. Our objective is to separate the episodes into snoring sounds and non-snoring sounds. We perform further analysis of the extracted snoring sounds to check if the testee has apnea. With polysomnogram data, we detect the relationship between snoring sounds and other sleep signals such as snoring vibration, sleep stages and body position.
| Identifer | oai:union.ndltd.org:CHENGCHI/G0937530041 |
| Creators | 蘇以暄, Su, Yi-Syuan |
| Publisher | 國立政治大學 |
| Source Sets | National Chengchi University Libraries |
| Language | 英文 |
| Detected Language | English |
| Type | text |
| Rights | Copyright © nccu library on behalf of the copyright holders |
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