Music emotion recognition:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla.
CRC
2011
|
| Series: | Multimedia computing, communication and intelligence
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XIV, 247 S. Ill., graph. Darst. |
| ISBN: | 9781439850466 1439850461 |
Staff View
MARC
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| 245 | 1 | 0 | |a Music emotion recognition |c by Yi-Hsuan Yang and Homer H. Chen |
| 264 | 1 | |a Boca Raton, Fla. |b CRC |c 2011 | |
| 300 | |a XIV, 247 S. |b Ill., graph. Darst. | ||
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| 490 | 0 | |a Multimedia computing, communication and intelligence | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Musik | |
| 650 | 4 | |a Music |x Mathematical models | |
| 650 | 4 | |a Emotions |x Mathematical models | |
| 650 | 4 | |a Information storage and retrieval systems |x Music | |
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Record in the Search Index
| _version_ | 1804147998162157568 |
|---|---|
| adam_text | Contents
Preface
.................................................................................................xi
Abbreviations
....................................................................................xiii
1
Introduction
.................................................................................1
1.1
Importance of Music Emotion Recognition
......................................1
1.2
Recognizing the Perceived Emotion of Music
....................................4
1.3
Issues of Music Emotion Recognition
...............................................6
1.3.1
Ambiguity and Granularity of Emotion Description
............6
1.3.2
Heavy Cognitive Load of Emotion Annotation
....................7
1.3.3
Subjectivity of Emotional Perception
...................................8
1.3.4
Semantic Gap between Low-Level Audio Signal
and High-Level Human Perception
.....................................9
1.4
Summary
.........................................................................................12
2
Overview of Emotion Description and Recognition
...............15
2.1
Emotion Description
.......................................................................15
2.1.1
Categorical Approach
........................................................16
2.1.2
Dimensional Approach
......................................................18
2.1.3
Music Emotion Variation Detection
.................................20
2.2
Emotion Recognition
......................................................................21
2.2.1
Categorical Approach
........................................................22
2.2.1.1
Data Collection
.................................................23
2.2.1.2
Data Preprocessing
............................................25
2.2.1.3
Subjective Test
..................................................26
2.2.1.4
Feature Extraction
.............................................28
2.2.1.5
Model Training
.................................................28
2.2.2
Dimensional Approach
......................................................29
2.2.3
Music Emotion Variation Detection
.................................31
2.3
Summary
.........................................................................................
*2
vi
■ Contents
3
Music Features
...........................................................................35
3.1
Energy Features
...............................................................................36
3.2
Rhythm Features
.............................................................................37
3.3
Temporal Features
..........................................................................42
3.4
Spectrum Features
...........................................................................44
3.5
Harmony Features
...........................................................................51
3.6
Summary
.........................................................................................54
4
Dimensional
MER
by Regression
.............................................55
4.1
Adopting the Dimensional Conceptualization
of Emotion
......................................................................................55
4.2
VA
Prediction
.................................................................................57
4.2.1
Weighted Sum of Component Functions
..........................57
4.2.2
Fuzzy Approach
.................................................................58
4.2.3
System Identification Approach (System ID)
.....................58
4.3
The Regression Approach
................................................................59
4.3.1
Regression Theory
.............................................................59
4.3.2
Problem Formulation
........................................................60
4.3.3
Regression Algorithms
.......................................................60
4.3.3.1
Multiple Linear Regression
................................60
4.3.3.2
е
-Support
Vector Regression
.............................61
4.3.3.3
AdaBoost Regression Tree (AdaBoost.RT)
........62
4.4
System Overview
.............................................................................62
4.5
Implementation
..............................................................................63
4.5.1
Data Collection
.................................................................63
4.5.2
Feature Extraction
.............................................................65
4.5.3
Subjective Test
...................................................................67
4.5.4
Regressor Training
.............................................................67
4.6
Performance Evaluation
..................................................................68
4.6.1
Consistency Evaluation of the Ground Truth
....................68
4.6.2
Data Transformation
.........................................................70
4.6.3
Feature Selection
................................................................71
4.6.4
Accuracy of Emotion Recognition
.....................................74
4.6.5
Performance Evaluation for Music Emotion
Variation Detection
...........................................................77
4.6.6
Performance Evaluation for Emotion Classification
...........78
4.7
Summary
.........................................................................................79
5
Ranking-Based Emotion Annotation and Model Training
.....81
5.1
Motivation
......................................................................................81
5.2
Ranking-Based Emotion Annotation
...............................................82
Contents
ш
vii
5.3
Computational Model for Ranking Music
by Emotion
.....................................................................................84
5.3.1
Learning-to-Rank
..............................................................85
5.3.2
Ranking Algorithms
...........................................................85
5.3.2.1
RankSVM
.........................................................85
5.3.2.2
ListNet
..............................................................85
5.3.2.3
RBF-ListNet
......................................................87
5.4
System Overview
.............................................................................90
5.5
Implementation
..............................................................................90
5.5.1
Data Collection
.................................................................92
5.5.2
Feature Extraction
.............................................................95
5.6
Performance Evaluation
..................................................................96
5.6.1
Cognitive Load of Annotation
...........................................97
5.6.2
Accuracy of Emotion Recognition
.....................................98
5.6.2.1
Comparison of Different Feature
Representations
.................................................99
5.6.2.2
Comparison of Different Learning
Algorithms
......................................................100
5.6.2.3
Sensitivity Test
................................................102
5.6.3
Subjective Evaluation of the Prediction Result
.................104
5.7
Discussion
.....................................................................................104
5.8
Summary
.......................................................................................105
Fuzzy Classification of Music Emotion
..................................107
6.1
Motivation
....................................................................................107
6.2
Fuzzy Classification
.......................................................................108
6.2.1
Fuzzy ¿-NN Classifier
.....................................................108
6.2.2
Fuzzy Nearest-Mean Classifier
.........................................109
6.3
System Overview
...........................................................................112
6.4
Implementation
............................................................................113
6.4.1
Data Collection
...............................................................113
6.4.2
Feature Extraction and Feature Selection
.........................113
6.5
Performance Evaluation
................................................................114
6.5.1
Accuracy of Emotion Classification
..................................114
6.5.2
Music Emotion Variation Detection
................................114
6.6
Summary
.......................................................................................
^?
f
Personalized
MER
and Groupwise
MER
.................................119
7.1
Motivation
....................................................................................
H9
7.2
Personalized
MER
.........................................................................
121
7.3
Groupwise
MER
...........................................................................
122
viii
■ Contents
7.4 Implementation............................................................................124
7.4.1 Data
Collection...............................................................
124
7.4.2 Personal Information
Collection
......................................126
7.4.3 Feature
Extraction
...........................................................127
7.5 Performance Evaluation................................................................128
7.5.1 Performance
of the
General
Method................................
128
7.5.2 Performance
of GWMER
................................................130
7.5.3 Performance
of PMER
.....................................................130
7.6
Summary
.......................................................................................134
8
Two-Layer Personalization
.....................................................135
8.1
Problem Formulation
....................................................................135
8.2
Bag-of-Users Model
......................................................................136
8.3
Residual Modeling and Two-Layer Personalization Scheme
..........137
8.4
Performance Evaluation
................................................................139
8.5
Summary
.......................................................................................143
9
Probability Music Emotion Distribution Prediction
.............145
9.1
Motivation
....................................................................................145
9.2
Problem Formulation
....................................................................146
9.3
The KDE-Based Approach to Music Emotion
Distribution Prediction
.................................................................148
9.3.1
Ground Truth Collection
................................................148
9.3.2
Regressor Training
...........................................................150
9.3.2.1
y-Support Vector Regression
...........................151
9.3.2.2
Gaussian Process Regression
............................151
9.3.3
Regressor Fusion
..............................................................153
9.3.3.1
Weighted by Performance
...............................153
9.3.3.2
Optimization
...................................................154
9.3.4
Output of Emotion Distribution
.....................................156
9.4
Implementation
............................................................................157
9.4.1
Data Collection
...............................................................157
9.4.2
Feature Extraction
...........................................................157
9.5
Performance Evaluation
................................................................161
9.5.1
Comparison of Different Regression Algorithms
..............161
9.5.2
Comparison of Different Distribution
Modeling Methods
..........................................................162
9.5.3
Comparison of Different Feature Representations
...........165
9.5.4
Evaluation of Regressor Fusion
........................................166
9.6
Discussion
.....................................................................................167
9.7
Summary
.......................................................................................172
Contents
ш
ix
10
Lyrics Analysis and Its Application to
MER
...........................173
10.1
Motivation
..................................................................................173
10.2
Lyrics Feature Extraction
.............................................................174
10.2.1
Uni-Gram
....................................................................175
10.2.2
Probabilistic Latent Semantic Analysis (PLSA)
.............176
10.2.3
Bi-Gram
.......................................................................177
10.3 Multimodal
MER
System
...........................................................179
10.4
Performance Evaluation
..............................................................181
10.4.1
Comparison of
Multimodal
Fusion Methods
...............181
10.4.2
Performance of PLSA Model
........................................183
10.4.3
Performance of Bi-Gram Model
...................................184
10.5
Summary
.....................................................................................184
11
Chord Recognition and Its Application to
MER
.....................187
11.1
Chord Recognition
.....................................................................187
11.1.1
Beat Tracking and
PCP
Extraction
...............................188
11.1.2
Hidden Markov Model and N-Gram Model
................188
11.1.3
Chord Decoding
...........................................................190
11.2
Chord Features
............................................................................191
11.2.1
Longest Common Chord Subsequence
.........................192
11.2.2
Chord Histogram
.........................................................192
11.3
System Overview
.........................................................................193
11.4
Performance Evaluation
..............................................................193
11.4.1
Evaluation of Chord Recognition System
.....................193
11.4.2
Accuracy of Emotion Classification
..............................194
11.5
Summary
.....................................................................................196
12
Genre Classification and Its Application to
MER
...................197
12.1
Motivation
..................................................................................197
12.2
Two-Layer Music Emotion Classification
...................................198
12.3
Performance Evaluation
..............................................................199
12.3.1
Data Collection
............................................................199
12.3.2
Analysis of the Correlation between Genre
and Emotion
.................................................................200
12.3.3
Evaluation of the Two-Layer Emotion
Classification Scheme
...................................................203
12.3.3.1
Computational Model
................................203
12.3.3.2
Evaluation Measures
...................................203
12.3.3.3
Results
........................................................204
12.4
Summary
.....................................................................................205
Contents
13
Music Retrieval in the Emotion Plane
....................................207
13.1
Emotion-Based Music Retrieval
..................................................207
13.2
2D Visualization of Music
...........................................................208
13.3
Retrieval Methods
.......................................................................208
13.3.1
Query by Emotion Point (QBEP)
................................209
13.3.2
Query by Emotion Trajectory (QBET)
........................209
13.3.3
Query by Artist and Emotion (QBAE)
.........................209
13.3.4
Query by Lyrics and Emotion (QBLE)
.........................209
13.4
Implementation
..........................................................................210
13.5
Summary
.....................................................................................212
14
Future Research Directions
.....................................................213
14.1
Exploiting Vocal Timbre for
MER
..............................................213
14.2
Emotion Distribution Prediction Based on Rankings
..................214
14.3
Personalized Emotion-Based Music Retrieval
..............................215
14.4
Situational Factors of Emotion Perception
..................................215
14.5
Connections between Dimensional and Categorical
MER
..........216
14.6
Music Retrieval and Organization in
3D
Emotion Space
............216
References
.........................................................................................219
Index
..................................................................................................237
|
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| ctrlnum | (OCoLC)742006734 (DE-599)BVBBV039153323 |
| discipline | Informatik |
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| id | DE-604.BV039153323 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:00:08Z |
| institution | BVB |
| isbn | 9781439850466 1439850461 |
| language | English |
| lccn | 2011560838 |
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| record_format | marc |
| series2 | Multimedia computing, communication and intelligence |
| spelling | Yang, Yi-Hsuan Verfasser aut Music emotion recognition by Yi-Hsuan Yang and Homer H. Chen Boca Raton, Fla. CRC 2011 XIV, 247 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Multimedia computing, communication and intelligence Mathematisches Modell Musik Music Mathematical models Emotions Mathematical models Information storage and retrieval systems Music Data Mining (DE-588)4428654-5 gnd rswk-swf Emotionales Verhalten (DE-588)4152089-0 gnd rswk-swf Musikwahrnehmung (DE-588)4126097-1 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Musikwahrnehmung (DE-588)4126097-1 s Data Mining (DE-588)4428654-5 s Emotionales Verhalten (DE-588)4152089-0 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Chen, Homer H. Sonstige oth Digitalisierung BSB Muenchen 4 application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024171057&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Yang, Yi-Hsuan Music emotion recognition Mathematisches Modell Musik Music Mathematical models Emotions Mathematical models Information storage and retrieval systems Music Data Mining (DE-588)4428654-5 gnd Emotionales Verhalten (DE-588)4152089-0 gnd Musikwahrnehmung (DE-588)4126097-1 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4152089-0 (DE-588)4126097-1 (DE-588)4114528-8 |
| title | Music emotion recognition |
| title_auth | Music emotion recognition |
| title_exact_search | Music emotion recognition |
| title_full | Music emotion recognition by Yi-Hsuan Yang and Homer H. Chen |
| title_fullStr | Music emotion recognition by Yi-Hsuan Yang and Homer H. Chen |
| title_full_unstemmed | Music emotion recognition by Yi-Hsuan Yang and Homer H. Chen |
| title_short | Music emotion recognition |
| title_sort | music emotion recognition |
| topic | Mathematisches Modell Musik Music Mathematical models Emotions Mathematical models Information storage and retrieval systems Music Data Mining (DE-588)4428654-5 gnd Emotionales Verhalten (DE-588)4152089-0 gnd Musikwahrnehmung (DE-588)4126097-1 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Mathematisches Modell Musik Music Mathematical models Emotions Mathematical models Information storage and retrieval systems Music Data Mining Emotionales Verhalten Musikwahrnehmung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024171057&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT yangyihsuan musicemotionrecognition AT chenhomerh musicemotionrecognition |