Music Data Analysis

Dietmar Jannach, Günter Rudolphm and Igor Vatolkin are affiliated with the Department of Computer Science, TU Dortmund University, GermanyClaus Weihs is affiliated with the Department of Statistics at TU Dortmund University, Germany

" . . . what makes this book unique is that it covers a much broader range of topics. Not only does it present a concrete tutorial on signal processing and music information retrieval . . . , but it also talks about interesting topics such as emotions, automatic composition, hardware, and others, so readers are sure to find novel information . . . In summary, MusicDataAnalysis is well thought-out and well written. It chooses to emphasize a breadth of topics rather than specialize in specific ones. This book nicely accomplishes its goal of serving as an introductory textbook for music research. It is also a very useful reference and valuable resource for individuals seeking new directions in the field." ~Yupeng Gu, Journal of the American Statistical Association". . . the book is impressive in its structure, comprehensiveness, clarity and accuracy. . . This text has staked out a specialised interdisciplinary niche, but as a self-contained guide to computational methods for music, I think it unlikely to be surpassed in the near future." ~David Bulger, Australian & New Zealand Journal of Statistics"Theoretical and practical exercises based on R and MATLAB are provided in the book’s web site, as well as example data sets. The book is very clearly written, and the style is fairly uniform despite the large number of authors. In sum, a very useful and enjoyable book." ~Ricardo Maronna, Stat Papers

• MUSIC AND AUDIO. Introduction. The Musical Signal - Physically and Psychologically. Musical Structures and Their Perception. Digital Signal Processing. Digital Representation of Music. Signal-level Features. METHODS. Foundations of Statistics. Optimization. Unsupervised Classification. Supervised Classification. Evaluation. Feature Processing. Feature Selection. APPLICATIONS. Transcription. Segmentation. Instrument Recognition. Chord and Harmony Recognition. Tempo Recognition. Emotions. Structuring Of Music Collections. Music Recommendation. Automatic Composition. IMPLEMENTATION. Architecture. User Interaction. Hardware.