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E-raamat: Music Similarity and Retrieval: An Introduction to Audio- and Web-based Strategies

  • Formaat: PDF+DRM
  • Sari: The Information Retrieval Series 36
  • Ilmumisaeg: 28-May-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783662497227
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  • Formaat: PDF+DRM
  • Sari: The Information Retrieval Series 36
  • Ilmumisaeg: 28-May-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783662497227
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This book provides a summary of the manifold audio- and web-based approaches to music information retrieval (MIR) research. In contrast to other books dealing solely with music signal processing, it addresses additional cultural and listener-centric aspects and thus provides a more holistic view. Consequently, the text includes methods operating on features extracted directly from the audio signal, as well as methods operating on features extracted from contextual information, either the cultural context of music as represented on the web or the user and usage context of music.





Following the prevalent document-centered paradigm of information retrieval, the book addresses models of music similarity that extract computational features to describe an entity that represents music on any level (e.g., song, album, or artist), and methods to calculate the similarity between them. While this perspective and the representations discussed cannot describe all musical dimensions, theyenable us to effectively find music of similar qualities by providing abstract summarizations of musical artifacts from different modalities.





The text at hand provides a comprehensive and accessible introduction to the topics of music search, retrieval, and recommendation from an academic perspective. It will not only allow those new to the field to quickly access MIR from an information retrieval point of view but also raise awareness for the developments of the music domain within the greater IR community. In this regard, Part I deals with content-based MIR, in particular the extraction of features from the music signal and similarity calculation for content-based retrieval. Part II subsequently addresses MIR methods that make use of the digitally accessible cultural context of music. Part III addresses methods of collaborative filtering and user-aware and multi-modal retrieval, while Part IV explores current and future applications of music retrieval and recommendation.>

Arvustused

Knees and Schedls book on Music similarity and retrieval differs from other books on the topic in that the authors have focused on music rather than acoustical signal processing, adding several cultural- and listener-centric aspects thereby rendering a holistic view. As a result, we find not only methods working on musical characteristics retrieved from the audio signal, but also techniques working on characteristics obtained from contextual information Although not a textbook, I would definitely recommend it as handy reference material for music researchers, postgraduate students, and teachers of music or musicology. (Soubhik Chakraborty, Computing Reviews, February, 2017)

1 Introduction to Music Similarity and Retrieval
1(32)
1.1 Music Information Retrieval
2(1)
1.2 MIR from an Information Retrieval Perspective
3(9)
1.2.1 Retrieval Tasks and Applications in MIR
4(2)
1.2.2 Browsing Interfaces in MIR
6(3)
1.2.3 Recommendation Tasks and Applications in MIR
9(1)
1.2.4 MIR Beyond Retrieval, Browsing, and Recommendation
10(2)
1.3 Music Similarity
12(6)
1.3.1 Computational Factors of Music Similarity
13(1)
1.3.2 Music Features
14(4)
1.4 Contents of this Book
18(1)
1.5 Evaluation of Music Similarity Algorithms
19(11)
1.5.1 Evaluation Using Prelabeled Data
20(4)
1.5.2 Evaluation Using Human Judgments
24(2)
1.5.3 Evaluation Using Listening Histories
26(1)
1.5.4 Music Collection and Evaluation in this Book
26(4)
1.6 Further Reading
30(3)
Part I Content-Based MIR
2 Basic Methods of Audio Signal Processing
33(18)
2.1 Categorization of Acoustic Music Features
33(3)
2.2 Simplified Scheme of a Music Content Feature Extractor
36(5)
2.2.1 Analog-Digital Conversion
37(2)
2.2.2 Framing and Windowing
39(1)
2.2.3 Fourier Transform
40(1)
2.3 Common Low-Level Features
41(8)
2.3.1 Time Domain Features
43(4)
2.3.2 Frequency Domain Features
47(2)
2.4 Summary
49(1)
2.5 Further Reading
50(1)
3 Audio Feature Extraction for Similarity Measurement
51(34)
3.1 Psychoacoustic Processing
51(4)
3.1.1 Physical Measurement of Sound Intensity
52(1)
3.1.2 Perceptual Measurement of Loudness
52(1)
3.1.3 Perception of Frequency
53(2)
3.2 Frame-Level Features and Similarity
55(12)
3.2.1 Mel Frequency Cepstral Coefficients
55(3)
3.2.2 Statistical Summarization of Feature Vectors
58(1)
3.2.3 Vector Quantization
58(2)
3.2.4 Gaussian Mixture Models
60(5)
3.2.5 Single Gaussian Model
65(2)
3.3 Block-Level Features and Similarity
67(11)
3.3.1 Fluctuation Pattern
68(2)
3.3.2 Logarithmic Fluctuation Pattern
70(1)
3.3.3 Spectral Pattern
71(3)
3.3.4 Correlation Pattern
74(3)
3.3.5 Similarity in the Block-Level Feature Framework
77(1)
3.4 Hubness and Distance Space Normalization
78(3)
3.5 Summary
81(2)
3.6 Further Reading
83(2)
4 Semantic Labeling of Music
85(22)
4.1 Genre Classification
86(4)
4.2 Auto-tagging
90(4)
4.2.1 Differences to Classification
90(1)
4.2.2 Auto-Tagging Techniques
91(3)
4.3 Mood Detection and Emotion Recognition
94(8)
4.3.1 Models to Describe Human Emotion
94(5)
4.3.2 Emotion Recognition Techniques
99(3)
4.4 Summary
102(1)
4.5 Further Reading
103(4)
Part II Music Context-Based MIR
5 Contextual Music Meta-data: Comparison and Sources
107(26)
5.1 Web-Based Music Information Retrieval
108(4)
5.1.1 The Web as Source for Music Features
108(2)
5.1.2 Comparison with Content-Based Methods
110(1)
5.1.3 Applications Using Web Data
111(1)
5.2 Data Formats for Web-Based MIR
112(2)
5.3 Tags and Annotations
114(5)
5.3.1 Expert Annotations
115(1)
5.3.2 Collaborative Tagging
115(2)
5.3.3 Games with a Purpose
117(2)
5.4 Web Texts
119(9)
5.4.1 Web Pages Related to Music
120(6)
5.4.2 Biographies, Product Reviews, and Audio Blogs
126(1)
5.4.3 Microblogs
127(1)
5.5 Lyrics
128(3)
5.5.1 Analysis of Lyrics on the Web
128(1)
5.5.2 Retrieval and Correction
129(2)
5.6 Summary
131(1)
5.7 Further Reading
132(1)
6 Contextual Music Similarity, Indexing, and Retrieval
133(28)
6.1 Text-Based Features and Similarity Measures
133(11)
6.1.1 Vector Space Model
134(5)
6.1.2 Latent Semantic Indexing
139(3)
6.1.3 Applications of Latent Factor Approaches
142(2)
6.2 Text-Based Indexing and Retrieval
144(4)
6.2.1 Pseudo Document Indexing
145(1)
6.2.2 Document-Centered Rank-Based Scoring
146(1)
6.2.3 Auto-Tag Indexing
147(1)
6.3 Similarity Based on Co-occurrences
148(3)
6.4 Combination with Audio Content Information
151(4)
6.4.1 Combined Similarity Measures
151(2)
6.4.2 Contextual Filtering
153(1)
6.4.3 Combined Tag Prediction
154(1)
6.5 Stylistic Analysis and Similarity
155(1)
6.6 Summary
156(1)
6.7 Further Reading
157(4)
Part III User-Centric MIR
7 Listener-Centered Data Sources and Aspects: Traces of Music Interaction
161(18)
7.1 Definition and Comparison of Listener-Centered Features
161(2)
7.2 Personal Collections and Peer-to-Peer Network Folders
163(1)
7.3 Listening Histories and Playlists
164(5)
7.4 User Ratings
169(1)
7.5 Modeling User Context
170(4)
7.5.1 Sensor Data for Modeling User Context
170(3)
7.5.2 Social Networks and User Connections
173(1)
7.6 Factors of User Intentions
174(2)
7.7 Summary
176(1)
7.8 Further Reading
177(2)
8 Collaborative Music Similarity and Recommendation
179(36)
8.1 Similarity Estimation via Co-occurrence
180(2)
8.2 Graph-Based and Distance-Based Similarity
182(4)
8.3 Exploiting Latent Context from Listening Sessions
186(5)
8.3.1 Latent Dirichlet Allocation
186(1)
8.3.2 Case Study: Artist Clustering from Listening Events
187(3)
8.3.3 Music Recommendation
190(1)
8.4 Learning from Explicit and Implicit User Feedback
191(7)
8.4.1 Memory-Based Collaborative Filtering
192(2)
8.4.2 Model-Based Collaborative Filtering
194(4)
8.5 Multimodal Combination
198(10)
8.5.1 Hybrid Recommender Systems
198(7)
8.5.2 Unified Metric Learning
205(3)
8.6 Summary
208(2)
8.7 Further Reading
210(5)
Part IV Current and Future Applications of MIR
9 Applications
215(32)
9.1 Music Information Systems
215(3)
9.1.1 Band Members and Their Roles
216(1)
9.1.2 Artist's or Band's Country of Origin
216(1)
9.1.3 Album Cover Artwork
217(1)
9.1.4 Data Representation
218(1)
9.2 User Interfaces to Music Collections
218(15)
9.2.1 Map-Based Interfaces
218(12)
9.2.2 Other Intelligent Interfaces
230(3)
9.3 Automatic Playlist Generation
233(6)
9.4 Music Popularity Estimation
239(6)
9.4.1 Popularity Estimation from Contextual Data Sources
240(4)
9.4.2 Comparison of Data Sources
244(1)
9.5 Summary
245(2)
10 Grand Challenges and Outlook
247(8)
10.1 Major Challenges
247(5)
10.1.1 Methodological Challenges
248(1)
10.1.2 Data-Related Challenges
249(1)
10.1.3 User-Centric Challenges
250(1)
10.1.4 General Challenges
251(1)
10.2 Future Directions
252(3)
A Description of the Toy Music Data Set
255(11)
A.1 Electronic Music
255(2)
A.2 Classical Music
257(2)
A.3 Heavy Metal
259(2)
A.4 Rap
261(2)
A.5 Pop
263(3)
References 266(27)
Index 293
Peter Knees holds a doctorate degree in computer science and is currently assistant professor of the Department of Computational Perception of the Johannes Kepler University Linz in Austria. For over a decade, he has been an active member of the music information retrieval research community, branching out to the related areas of multimedia, text IR, recommender systems, and digital media arts.





Markus Schedl is an associate professor of the Johannes Kepler University Linz / Department of Computational Perception. His main research interests include music and multimedia information retrieval, web and social media mining, and recommender systems. In addition to regularly publishing in and offering scientific services to top-tier conferences and journals of these fields, he is associate editor of the Springer International Journal of Multimedia Information Retrieval. He is also a keen lecturer and taught classes at the Universitat Pompeu Fabra Barcelona, QueenMary University London, and Kungliga Tekniska Högskolan Stockholm, among others.