音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 音樂在人類的歷史中扮演著舉足輕重角色。可影響情緒與績效， 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 因此被廣泛地應用在提升績效上，如運動或醫療。例慢跑者聆聽適當的音樂 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 可以跑得更久。然而，大多時候人們僅會根據個喜好選擇歌曲聆聽如此一 來，便降低了音樂所能達成的預期效 來，便降低了音樂所能達成的預期效 來，便降低了音樂所能達成的預期效 果。 此外，手動挑歌亦是件相當耗時的行為果。 此外，手動挑歌亦是件相當耗時的行為果。 此外，手動挑歌亦是件相當耗時的行為果。 此外，手動挑歌亦是件相當耗時的行為果。 此外，手動挑歌亦是件相當耗時的行為果。 此外，手動挑歌亦是件相當耗時的行為因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 因此，從如眾多的音樂中找出符合自己當下情緒時也更加困難。本研 究欲建立一智慧型選曲系統，本研方法以音樂資訊索引為基礎其可用來搜尋 究欲建立一智慧型選曲系統，本研方法以音樂資訊索引為基礎其可用來搜尋 究欲建立一智慧型選曲系統，本研方法以音樂資訊索引為基礎其可用來搜尋 究欲建立一智慧型選曲系統，本研方法以音樂資訊索引為基礎其可用來搜尋 究欲建立一智慧型選曲系統，本研方法以音樂資訊索引為基礎其可用來搜尋 與檢索特定類型的音樂。最後，將其以 APP 的方式呈現。此系統不僅能透過觀 測受者之心跳變異率，選擇適合使用聆聽音樂亦能情緒維持 測受者之心跳變異率，選擇適合使用聆聽音樂亦能情緒維持 測受者之心跳變異率，選擇適合使用聆聽音樂亦能情緒維持 測受者之心跳變異率，選擇適合使用聆聽音樂亦能情緒維持 測受者之心跳變異率，選擇適合使用聆聽音樂亦能情緒維持 在舒緩愉悅的狀態。這項研究期望提供有效音樂模式，助於治療創作和 在舒緩愉悅的狀態。這項研究期望提供有效音樂模式，助於治療創作和 在舒緩愉悅的狀態。這項研究期望提供有效音樂模式，助於治療創作和 在舒緩愉悅的狀態。這項研究期望提供有效音樂模式，助於治療創作和 在舒緩愉悅的狀態。這項研究期望提供有效音樂模式，助於治療創作和 音樂療法的相關領域應用。

Music plays an important role in our daily lives. Since music could affect people’s emotion, it has been widely applied to works or sports to enhance the performance of some particular tasks. For example, workers performed better when listening to music, joggers ran longer distance with proper music. However, people usually select music according to their personal preferences. Thus, it might reduce the effect. Therefore, this study aims to establish an intelligent music selection system to provide appropriate songs for users who work at home to enhance their performance. This study established an emotional music database through the classification of data mining. Innovative wearable sensing and computing system were applied to detect heart rate variability. Users’ emotions were predicted and appropriate songs were selected by application software (APP). Machine learning was employed to record the preference of users so as to accurate the precision of classification. The system could select appropriate music automatically by tracing the user’s heart rate variability. More importantly, through experimental validation, this system had been proved has high satisfaction and might not increase mental workload, keep users pleasant and improve performance. With the concept of IoT (Internet of Things) and implantation of diversified developed wearable devices, this system could provide more innovative applications for smart factory, smart home and health care.

Table of Contents
Abstract IV
Table of Contents V
List of Figures VII
List of Tables IX
1 Introduction 1
2 Literature Review 2
2.1 Music and Emotion 2
2.2 Emotion and Heart Rate Variability 3
2.3 Music features 6
2.4 Machine learning 9
2.5 Summary 10
3 Methodology 11
3.1 Phase I: Music Database Establishment 12
3.2 Phase II: Intelligent Music Selection Construction 15
4 Case Study 26
4.1 Phase I Emotional Music Database Establishment 26
4.2 Phase II Intelligent Music Selection System Establishment 33
4.3 Discussion 50
5 Conclusion 54
6 References 55
Appendix 59
Appendix A The analyzed result of training data 59
Appendix B The analyzed result of testing data 60
Appendix C The classified result of testing data 74
Appendix D The classified result of positive music 75
Appendix E The experiment results of SDNN signals 76
Appendix F The results of system usability scale questionnaires 77
Appendix G Reliability and validity of questionnaire 77
Appendix H The results of satisfaction questionnaires 78
Appendix I The results of NASA-TLX questionnaires 78
Appendix J The measurement model analysis 79
Appendix K The certification of IRB 82

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