Implementation of SNR

Vemuri priyarequested to merge
speaker-diarization into speaker-diarization_1

Description

This Merge Request implements the Signal-to-Noise Ratio (SNR) calculation feature for the audio processing pipeline. The functionality enables analysis of audio quality by measuring the ratio between the desired signal strength and background noise levels.

Features Implemented

  • Added SNR calculation functionality for uploaded/processed audio files.
  • Computed signal power and noise power from audio samples.
  • Generated SNR values in decibels (dB).
  • Integrated SNR results into the backend response structure.
  • Added handling for invalid or low-quality audio inputs.
  • Improved modularity for future audio analysis extensions.

Technical Details

  • Processed audio signals using Python audio-processing utilities.
  • Implemented mathematical computation for:
SNR(dB) = 10 * log10(Signal Power / Noise Power)
  • Structured the implementation for compatibility with future spectrogram and advanced audio analytics features.

Technologies Used

  • Python
  • Audio Processing Libraries
  • NumPy
  • FastAPI Backend Integration

Outcome

This implementation enables quantitative audio quality assessment and provides a foundation for future enhancements such as:

  • Spectrogram analysis
  • Noise reduction
  • Audio quality monitoring
  • Voice analytics

Testing Performed

  • Verified SNR calculations on sample audio files.
  • Tested backend response integration.
  • Validated SNR output formatting in decibels.
  • Checked handling of noisy and silent audio samples.
  • Performed edge-case testing for invalid audio inputs.

Future Improvements

  • Add real-time SNR visualization
  • Integrate spectrogram analysis
  • Implement noise filtering techniques
  • Add configurable SNR threshold ranges
  • Improve audio preprocessing for enhanced accuracy

Closes #25 (closed)

Edited by Vemuri priya

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