Implement Signal-to-Noise Ratio (SNR)

Description

Implement Signal-to-Noise Ratio (SNR) calculation functionality for audio processing to measure the quality of audio signals by comparing signal strength against background noise levels.

Problem Statement

Currently, the system does not provide any quantitative measurement of audio quality or background noise levels. Without SNR analysis, it is difficult to evaluate the clarity and reliability of processed audio files.

There is a need to:

  • Measure audio quality programmatically
  • Detect noisy audio samples
  • Provide numerical audio quality metrics
  • Prepare the system for advanced audio analytics features

Proposed Solution

Implement an SNR calculation module that:

  • Processes audio input files
  • Separates signal and noise components
  • Calculates signal power and noise power
  • Computes SNR values in decibels (dB)

Formula Used

SNR(dB)=10\log_{10}\left(\frac{Signal\ Power}{Noise\ Power}\right)

Features to Implement

  • Audio signal processing
  • SNR computation in dB
  • Backend integration for returning SNR values
  • Error handling for invalid or silent audio
  • Modular structure for future spectrogram analysis integration

Expected Outcome

  • Accurate SNR calculation for audio files
  • Improved audio quality analysis
  • Numerical quality metrics for processed audio
  • Foundation for future audio analytics features such as:
    • Spectrogram analysis
    • Noise reduction
    • Voice quality assessment

Technologies Suggested

  • Python
  • NumPy
  • Audio Processing Libraries
  • FastAPI Backend

Acceptance Criteria

  • Audio files are processed successfully
  • SNR values are calculated correctly
  • Results are returned in decibel (dB) format
  • Invalid audio inputs are handled properly
  • Backend response includes SNR metrics
  • Code is modular and maintainable