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Utilising temporal signal features in adverse noise conditions: Detection, estimation, and the reassigned spectrogram
1Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, United Kingdom.
The Journal of the Acoustical Society of America
|March 4, 2016
Summary
This study introduces new detectors for passive sonar, analyzing how noise impacts temporal fine structure measurements. Fine structure detectors, especially when combined with power measurements, outperform traditional methods for detecting signals in noise.
Area of Science:
- Signal processing
- Acoustics
- Time-frequency analysis
Background:
- Fourier spectrograms are standard for passive sonar, relying on signal and noise power statistics.
- Time-frequency representations enhance sparse signal detection but nonlinearities complicate noise analysis.
- Temporal fine structure is sensitive to additive noise, impacting detection models.
Purpose of the Study:
- Analyze the effect of additive noise on temporal fine structure measurements.
- Develop and evaluate novel detectors based on fine structure for signal detection.
- Compare fine structure detectors against traditional power-based detectors.
Main Methods:
- Developed detectors using zero crossing intervals, peak amplitudes, and instantaneous frequency.
- Evaluated detector performance for sinusoid detection in Gaussian noise.
- Established the power detector as a baseline for comparison.
Main Results:
- Fine structure detectors showed superior performance under specific conditions compared to the power detector.
- Combined fine structure and power measurement detectors demonstrated the best overall performance.
- Reassigned spectrograms' assumption of reliable statistics for energy reassignment was challenged.
Conclusions:
- Temporal fine structure measurements offer valuable information for signal detection in passive sonar.
- Novel detectors integrating fine structure and power measurements provide enhanced detection capabilities.
- A doubly reassigned spectrogram concept is proposed, leveraging noise statistical models for improved temporal measurements.
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