Continuous Music Generation Using Video Based on Circumplex Model of Affect

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研究生：	MOLION SURYA PRADANA MOLION SURYA PRADANA
論文名稱：	Continuous Music Generation Using Video Based on Circumplex Model of Affect Continuous Music Generation Using Video Based on Circumplex Model of Affect
指導教授：	楊傳凱 Chuan-Kai Yang
口試委員:	賴源正 Yuan-Cheng Lai 林伯慎 Bor-Shen Lin
學位類別：	碩士 Master
系所名稱：	管理學院 - 資訊管理系 Department of Information Management
論文出版年：	2023
畢業學年度：	111
語文別：	英文
論文頁數：	59
外文關鍵詞：	Continuous music generation, Russell Circumplex model, Valence-arousal, Multimodal approach
相關次數：	點閱：140 下載：0
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This study presents a novel approach to continuous music generation utilizing the
Russell Circumplex model's valence-arousal dimensions as a basis for emotional representation. The proposed system combines facial emotion recognition, transfer learning, and symbolic music generation to dynamically generate music based on continuously evolving emotional states. The process begins with face detection, followed by facial landmark modeling to predict the emotional state of the detected face. To account for situations where faces are not present, a transfer learning model is trained to classify images that lack facial features. The most effective transfer learning model identified in this study is EfficientNetB2_0.001_32, which
achieves promising results with root mean square error (RMSE) values of 0.327 for valence and 0.223 for arousal prediction. Next, the predicted valence-arousal values are used to condition the symbolic music generation system. This multimodal approach allows for the translation of continuous-valued emotions into corresponding musical characteristics, such as tempo, melody, and harmony. The music generation process occurs at regular intervals, with new musical output being generated every 60 seconds to adapt to the evolving emotional states. The proposed system demonstrates smooth operation and robustness in predicting valence and arousal values, as well as generating coherent music reflective of the detected emotions. The integration of facial emotion recognition and symbolic music generation provides a comprehensive framework for continuous music generation in real-time, with potential applications in various domains such as interactive media, entertainment, and therapy.

ABSTRACT    iv
ACKNOWLEDGEMENT    v
TABLES OF CONTENT    vi
LIST OF FIGURES    viii
LIST OF TABLES    ix
CHAPTER I INTRODUCTION    1
I.1 BACKGROUND    1
I.2 OBJECTIVE    3
I.3 CONTRIBUTIONS    3
I.4 RESEARCH OUTLINE    3
CHAPTER II LITERATURE REVIEW    5
II.1 FACE LANDMARKS DETECTION, FEATURE EXTRACTIONS, AND EXPRESSION RECOGNITION.    5
II.2 RUSSELL'S CIRCUMPLEX MODEL OF AFFECT    9
II.3 TRANSFER LEARNING IMAGENET MODEL    11
II.4 FLUIDSYNTH    15
II.5 PYDUB    16
II.6 PYGAME    17
II.7 SYMBOLIC MUSIC GENERATION    17
CHAPTER III PROPOSED METHOD    20
III.1 MODEL 1    20
III.1.1 OPEN AFFECTIVE STANDARDIZED IMAGE SET (OASIS) DATASET    20
III.1.2 DATA PRE-PROCESSING    21
III.1.3 DATA AUGMENTATION    21
III.1.4 ARCHITECTURE    22
III.1.5 TRAINING    24
III.1.6 PERFORMANCE EVALUATIONS    26
III.2 ARCHITECTURE OF CONTINUOUS MUSIC GENERATION    28
III.2.1 MODEL 2    29
III.2.2 MODEL 3    30
III.2.3 OPTIMATION FOR CONTINUOUS MUSIC GENERATION    34
CHAPTER IV EXPERIMENTS & RESULTS    36
ABSTRACT    iv
ACKNOWLEDGEMENT    v
TABLES OF CONTENT    vi
LIST OF FIGURES    viii
LIST OF TABLES    ix
CHAPTER I INTRODUCTION    1
I.1 BACKGROUND    1
I.2 OBJECTIVE    3
I.3 CONTRIBUTIONS    3
I.4 RESEARCH OUTLINE    3
CHAPTER II LITERATURE REVIEW    5
II.1 FACE LANDMARKS DETECTION, FEATURE EXTRACTIONS, AND EXPRESSION RECOGNITION.    5
II.2 RUSSELL'S CIRCUMPLEX MODEL OF AFFECT    9
II.3 TRANSFER LEARNING IMAGENET MODEL    11
II.4 FLUIDSYNTH    15
II.5 PYDUB    16
II.6 PYGAME    17
II.7 SYMBOLIC MUSIC GENERATION    17
CHAPTER III PROPOSED METHOD    20
III.1 MODEL 1    20
III.1.1 OPEN AFFECTIVE STANDARDIZED IMAGE SET (OASIS) DATASET    20
III.1.2 DATA PRE-PROCESSING    21
III.1.3 DATA AUGMENTATION    21
III.1.4 ARCHITECTURE    22
III.1.5 TRAINING    24
III.1.6 PERFORMANCE EVALUATIONS    26
III.2 ARCHITECTURE OF CONTINUOUS MUSIC GENERATION    28
III.2.1 MODEL 2    29
III.2.2 MODEL 3    30
III.2.3 OPTIMATION FOR CONTINUOUS MUSIC GENERATION    34
CHAPTER IV EXPERIMENTS & RESULTS    36
IV.1 TRAINING    36
IV.1.1 WITHOUT DATA AUGMENTATION    36
IV.1.2 WITH DATA AUGMENTATION    37
IV.1.3 TRANSFER LEARNING WITH DATA AUGMENTATION    38
IV.1.4 TRANSFER LEARNING WITHOUT DATA AUGMENTATION    39
IV.2 RESULT OF CONTINUOUS MUSIC GENERATION    52
CHAPTER V CONCLUSION & FUTURE WORKS    55
V.1 CONCLUSIONS    55
V.2 FUTURE WORK    55
REFERENCES    56


                                

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全文公開日期 2025/08/20 (校內網路)
全文公開日期 2025/08/20 (校外網路)
全文公開日期 2025/08/20 (國家圖書館：臺灣博碩士論文系統)

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