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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Updated: Aug 23, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Classification of audio signals using spectrogram surfaces and extrinsic distortion measures.

Jeremy Levy1,2, Alexander Naitsat1, Yehoshua Y Zeevi1

  • 1Faculty of Electrical Engineering, Technion, Haifa, Israel.

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|October 31, 2022
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Summary

This study introduces a new method to analyze one-dimensional (1D) signals by representing them as geometric surfaces. This approach improves signal classification and feature extraction for audio signals like lung sounds and speech accents.

Keywords:
1D signal processingClassificationDistortions measureGeometric feature engineeringManifoldSpectrogram embeddingSurfaces

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Area of Science:

  • Signal processing
  • Geometric analysis
  • Machine learning

Background:

  • Analyzing one-dimensional (1D) signals benefits from geometric representations.
  • Existing methods for signal similarity assessment and classification can be improved.

Purpose of the Study:

  • To develop a robust algorithm for extracting geometric features from 1D signals.
  • To enhance signal analysis and classification through novel feature engineering.

Main Methods:

  • Representing 1D signals as geometric surfaces and higher-dimensional manifolds.
  • Mapping geometric objects into a reference domain for feature extraction.
  • Applying the approach to audio signal analysis, including lung sounds and accent detection.

Main Results:

  • The proposed algorithm extracts highly descriptive geometric features.
  • The method demonstrated superior performance compared to baseline models in signal classification tasks.
  • Applications in lung sound analysis and accent detection showed significant improvements.

Conclusions:

  • Geometric feature extraction offers a powerful tool for 1D signal analysis.
  • The novel algorithm enhances classification accuracy for audio signals.
  • Future work can explore higher-dimensional distortion measures for further extensions.