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Performance weighted blended spectrogram.

Jeff Tucker1, Kathleen E Wage1, John R Buck2

  • 1Electrical and Computer Engineering Department, George Mason University, Fairfax, Virginia 22030, USA.

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Summary
This summary is machine-generated.

This study introduces the performance weighted blended (PWB) spectrogram, an adaptive algorithm for time-frequency analysis. The PWB spectrogram efficiently separates signals and identifies quiet sources, even with limited computational resources.

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Area of Science:

  • Signal Processing
  • Time-Frequency Analysis
  • Machine Learning

Background:

  • Conventional spectrograms, based on tapered short-time Fourier transforms, involve a trade-off between resolution and interference suppression.
  • Adaptive spectrogram algorithms offer improved performance by adjusting filter banks to incoming data, but often require significant computational resources.
  • Existing adaptive methods present challenges for applications with limited computational power, such as autonomous systems.

Purpose of the Study:

  • To develop an adaptive spectrogram algorithm suitable for computationally constrained applications.
  • To enhance the separation of closely spaced tones and the detection of quiet signals.
  • To reduce the computational cost associated with adaptive time-frequency analysis.

Main Methods:

  • The performance weighted blended (PWB) spectrogram algorithm was developed.
  • PWB combines outputs from conventional filter banks with varying tapers.
  • Blend weights are adapted dynamically at each time-frequency point.

Main Results:

  • The PWB spectrogram effectively separates loud, closely spaced tones.
  • Quiet signals are successfully identified, even in the presence of noise.
  • The algorithm demonstrates significantly lower computational requirements compared to other adaptive methods.
  • Analysis of underwater glider data confirmed the PWB spectrogram's ability to detect a quiet chirp signal amidst vehicle noise.

Conclusions:

  • The PWB spectrogram provides an efficient adaptive solution for time-frequency analysis in resource-limited environments.
  • This algorithm offers a practical approach to improving signal detection and separation without prohibitive computational overhead.
  • The PWB spectrogram is well-suited for applications like autonomous vehicles and underwater acoustics.