Content-Based Methods for Knowledge Discovery in Music

  • Juan Pablo Bello
  • Peter Grosche
  • Meinard Müller
  • Ron Weiss
Part of the Springer Handbooks book series (SPRINGERHAND)

Abstract

This chapter presents several computational approaches aimed at supporting knowledge discovery in music. Our work combines data mining, signal processing and data visualization techniques for the automatic analysis of digital music collections, with a focus on retrieving and understanding musical structure.

We discuss the extraction of midlevel feature representations that convey musically meaningful information from audio signals, and show how such representations can be used to synchronize different instances of a musical work and enable new modes of music content browsing and navigation. Moreover, we utilize these representations to identify repetitive structures and representative patterns in the signal, via self-similarity analysis and matrix decomposition techniques that can be made invariant to changes of local tempo and key. We discuss how structural information can serve to highlight relationships within music collections, and explore the use of information visualization tools to characterize the patterns of similarity and dissimilarity that underpin such relationships.

With the help of illustrative examples computed on a collection of recordings of Frédéric Chopin’s Mazurkas, we aim to show how these content-based methods can facilitate the development of novel modes of access, analysis and interaction with digital content that can empower the study and appreciation of music.

2-D

two-dimensional

MFCC

Mel-frequency cepstral coefficient

MIDI

musical instrument digital interface

MIR

music information retrieval

NCD

normalized compression distance

PCP

pitch class profile

RCD

radial convergence diagram

SI-PLCA

shift-invariant probabilistic latent component analysis

SSM

self-similarity matrix

STFT

short-term Fourier transform/short-time Fourier transform

Notes

Acknowledgements

This material is based upon work supported by the National Science Foundation, under grant IIS-0844654, and the Cluster of Excellence on Multimodal Computing and Interaction at Saarland University. The authors would like to thank Craig Sapp for kindly providing access to the Mazurka dataset and beat annotations.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2018

Authors and Affiliations

  • Juan Pablo Bello
    • 1
  • Peter Grosche
    • 2
  • Meinard Müller
    • 3
  • Ron Weiss
    • 4
  1. 1.New York UniversityNew YorkUSA
  2. 2.Huawei Technologies Duesseldorf GmbHMünchenGermany
  3. 3.International Audio Laboratories ErlangenErlangenGermany
  4. 4.Google Inc.New YorkUSA

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