C. Weihs, D. Jannach, I. Vatolkin, G. Rudolph (Eds.):
Music Data Analysis: Foundations and Applications, CRC Press, November 2016
List of errata is available here.
Teaching material can be found here: Book with Exercises and Data Sets for Exercises.
To obtain the „Solutions Book“ for the exercises as an instructor, please contact orders@taylorandfrancis.com and provide information about the intended use of the book.
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Summary
This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology. |
| An excerpt from the review by Yupeng Gu in Journal of the American Statistical Association, 112:520, 1771-1783 (the complete review is available at https://doi.org/10.1080/01621459.2017.1411709):
[This book] demonstrates how a variety of problems—digitized data, musicology studies, music manufacturing, distribution, performance systems, and compositions—are related to almost every aspect of music in real life. It also provides overviews of sophisticated methodologies that researchers have developed to treat these problems over the past half century, demonstrating that this is a well-established area of scholarship. This book is an excellent guide for people interested in the area. |
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| List of chapters | |
| 1. Introduction (The editors) | |
| I. MUSIC AND AUDIO 2. The Musical Signal – Physically and Psychologically (S. Knoche, M. Ebeling) 3. Musical Structures and Their Perception (M. Ebeling) 4. Digital Filters and Spectral Analysis (R. Martin, A. Nagathil) 5. Signal-Level Features (A. Nagathil, R. Martin) 6. Auditory Models (K. Friedrichs, C. Weihs) 7. Digital Representation of Music (G. Rudolph) 8. Music Data: Beyond the Signal Level (D. Jannach, I. Vatolkin, G. Bonnin) |
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| II. METHODS 9. Statistical Methods (C. Weihs) 10. Optimization (G. Rudolph) 11. Unsupervised Learning (C. Weihs) 12. Supervised Classification (C. Weihs, T. Glasmachers) 13. Evaluation (I. Vatolkin, C. Weihs) 14. Feature Processing (I. Vatolkin) 15. Feature Selection (I. Vatolkin) |
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| III. APPLICATIONS 16. Segmentation (N. Bauer, S. Krey, U. Ligges, C. Weihs, I. Vatolkin) 17. Transcription (U. Ligges, C. Weihs) 18. Instrument Recognition (C. Weihs, K. Friedrichs, K. Wintersohl) 19. Chord Recognition (G. Peeters, J. Pauwels) 20. Tempo Estimation (J. R. Zapata) 21. Emotions (G. Rötter, I. Vatolkin) 22. Similarity-based Organization of Music Collections (S. Stober) 23. Music Recommendation (D. Jannach, G. Bonnin) 24. Automatic Composition (M. Hester, B. Kümper) |
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| IV. IMPLEMENTATION 25. Implementation Architectures (M. Botteck) 26. User Interaction (W. Theimer) 27. Hardware Architectures for Music Classification (I. Schmädecke, H. Blume) |
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