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Related Experiment Video

Updated: Jun 23, 2026

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

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Published on: December 20, 2024

Relative-pitch tracking of multiple arbitrary sounds.

Paris Smaragdis1

  • 1Adobe Systems Inc., 275 Grove Street, Newton, Massachusetts 02466, USA. paris@adobe.com

The Journal of the Acoustical Society of America
|May 12, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic method for tracking perceived pitch in complex sounds, including multiple sources and aperiodic signals. The approach demonstrates robust performance on real recordings, offering a novel alternative to traditional pitch tracking techniques.

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Area of Science:

  • Acoustics
  • Signal Processing
  • Computational Audition

Background:

  • Traditional pitch tracking methods struggle with aperiodic sounds and multiple simultaneous sound sources.
  • Defining and tracking pitch can be challenging for sounds with ill-defined spectral profiles.

Purpose of the Study:

  • To develop a robust probabilistic methodology for perceived pitch tracking.
  • To extend pitch tracking capabilities to aperiodic sounds and mixtures of simultaneous sources.
  • To model perceived pitch changes using a shift-invariant representation in the constant-Q domain.

Main Methods:

  • Utilized a probabilistic approach for pitch tracking.
  • Employed a shift-invariant representation in the constant-Q domain to model spectral shifts.
  • Developed a mixture model to enable simultaneous tracking of multiple sound sources.

Main Results:

  • Demonstrated the feasibility of tracking perceived pitch in potentially aperiodic sounds.
  • Successfully tracked pitch in mixtures of simultaneous sound sources.
  • Showcased the model's robustness under adverse conditions using real recordings.

Conclusions:

  • The probabilistic methodology offers a robust and versatile approach to pitch tracking.
  • The constant-Q domain representation enables pitch tracking even for sounds with ill-defined pitch.
  • The mixture model extends capabilities to complex acoustic scenes, outperforming traditional methods.