Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Independent component analysis for automatic note extraction from musical trills.

Judith C Brown1, Paris Smaragdis

  • 1Physics Department, Wellesley College, Wellesley, Massachusetts 02181, USA. brown@media.mit.edu

The Journal of the Acoustical Society of America
|May 14, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Dynamics and impact of footrot and climate on hoof horn length in 50 ewes from one farm over a period of 10 months.

Veterinary journal (London, England : 1997)·2014
Same author

Subcutaneously administered ofatumumab in rheumatoid arthritis: a phase I/II study of safety, tolerability, pharmacokinetics, and pharmacodynamics.

The Journal of rheumatology·2013
Same author

Automatic identification of individual killer whales.

The Journal of the Acoustical Society of America·2010
Same author

Hidden Markov and Gaussian mixture models for automatic call classification.

The Journal of the Acoustical Society of America·2009
Same author

Relative-pitch tracking of multiple arbitrary sounds.

The Journal of the Acoustical Society of America·2009
Same author

Evaluation of GW406381 for treatment of osteoarthritis of the knee: two randomized, controlled studies.

Medscape journal of medicine·2008
Same journal

High-resolution depth estimation for multiple wideband sources in deep sea via sparse Bayesian learninga).

The Journal of the Acoustical Society of America·2026
Same journal

Depression markers in speech: An approach based on tract variables dynamics.

The Journal of the Acoustical Society of America·2026
Same journal

The oyster toadfish (Opsanus tau) alters active and diurnal calling amid vessel noise in New York City.

The Journal of the Acoustical Society of America·2026
Same journal

Experimental noise characterisation of phase-locked tandem-rotor in edgewise flight.

The Journal of the Acoustical Society of America·2026
Same journal

The tune-text-temporal synergy: Prosodic effects of final segmental weakening in Neapolitan.

The Journal of the Acoustical Society of America·2026
Same journal

Monitoring vessel movement above critical offshore infrastructure using distributed acoustic sensing.

The Journal of the Acoustical Society of America·2026
See all related articles

Independent component analysis (ICA) significantly outperforms principal component analysis for separating musical sources from audio data. This advanced technique effectively extracts musical information, particularly from piano trills.

Area of Science:

  • Signal Processing
  • Music Information Retrieval
  • Machine Learning

Background:

  • Principal component analysis (PCA) is a traditional method for audio data redundancy reduction, relying on second-order statistics.
  • Existing methods struggle with effectively separating complex, independent musical sources within mixed audio signals.

Purpose of the Study:

  • To introduce and evaluate independent component analysis (ICA) as a superior method for musical source separation.
  • To demonstrate the effectiveness of ICA in extracting musical information from redundant audio data.

Main Methods:

  • Applied independent component analysis (ICA), a technique based on higher-order statistical independence.
  • Collected a database of piano trill rates from diverse sources: computer-driven piano, professional recordings, and commercial CDs.

Related Experiment Videos

  • Utilized ICA for automated extraction of musical features from audio signals.

    • Main Results:

    • Independent component analysis (ICA) proved significantly more effective than PCA in separating independent musical sources.
    • The application of ICA to piano trills demonstrated its capability in extracting detailed musical information.
    • Automated extraction of musical information from complex audio data was achieved.

    Conclusions:

    • Independent component analysis (ICA) is an outstanding and effective method for separating independent musical sources.
    • ICA offers a powerful approach for automatically extracting valuable musical information from large, redundant datasets.
    • This study highlights ICA's potential in advancing music information retrieval and analysis.