Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Best of 2016 Recommended by Editor Recommended by Reviewer Recommended by Reader
Search
Music similarity and retrieval : an introduction to audio- and web-based strategies
Knees P., Schedl M., Springer International Publishing, New York, NY, 2016. 305 pp.  Type: Book (978-3-662497-20-3)
Date Reviewed: Feb 9 2017

Knees and Schedl’s 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, either culturally represented or purely user defined.

The book has four technical parts other than an introductory chapter on music similarity and retrieval. Part 1, “Content-Based MIR” (music information retrieval), consists of basic methods of audio signal processing, audio feature extraction for similarity measurement, and semantic labeling of music (chapters 2 through 4). Some of the subtopics covered include analog-digital conversion; time domain and frequency domain features; Fourier transform; psychoacoustic processing of audio signals including physical measurements of loudness; frame-level similarity features, for example, mel-frequency cepstral coefficients (MFCC); and block-level similarity features like logarithmic or correlation patterns.

Part 2 covers music context-based MIR, which comprises comparisons and sources of contextual music metadata, contextual music similarity, and indexing and retrieval (chapters 5 and 6), where both web-based MIR and text-based aspects and similarity measures are addressed.

Part 3, “User-Centric MIR,” targets listener-centered features; collaborative music similarity and recommendation, including graph- and distance-based similarity; hybrid recommender systems; and so on (chapters 7 and 8).

Part 4 focuses on current and future applications of MIR. In particular, the applications include music information systems (for example, country of origin of an artist or a band), user interfaces to music collections, automatic playlist generation, and music popularity estimation (chapter 9). The future challenges addressed are methodological, data related, or user centric (chapter 10).

Appendix A gives “background information on the toy music dataset that [is used] as an example throughout the book.” This is followed by an extensive bibliography and a helpful index.

Although not a textbook, I would definitely recommend it as handy reference material for music researchers, postgraduate students, and teachers of music or musicology.

Reviewer:  Soubhik Chakraborty Review #: CR145056 (1705-0256)
Bookmark and Share
  Reviewer Selected
Editor Recommended
Featured Reviewer
 
 
Sound And Music Computing (H.5.5 )
 
 
Information Search And Retrieval (H.3.3 )
 
 
Similarity Measures (I.5.3 ... )
 
Would you recommend this review?
yes
no
Other reviews under "Sound And Music Computing": Date
Music through Fourier space: discrete Fourier transform in music theory
Amiot E.,  Springer International Publishing, New York, NY, 2016. 206 pp. Type: Book (978-3-319455-80-8)
May 25 2017
Language, music, and computing: First International Workshop, LMAC 2015, St. Petersburg, Russia, April 20-22, 2015, revised selected papers
Eismont P., Konstantinova N.,  Springer International Publishing, New York, NY, 2016. 177 pp. Type: Book (978-3-319274-97-3)
Jan 26 2017
Mathemusical conversations: mathematics and computation in music performance and composition
Smith J., Chew E., Assayag G.,  World Scientific Publishing Co, Inc., River Edge, NJ, 2016. 316 pp. Type: Book
Jan 25 2017
more...

E-Mail This Printer-Friendly
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright © 2000-2017 ThinkLoud, Inc.
Terms of Use
| Privacy Policy