Achieving Speech Enhancement through Noise Power Spectrum Estimation using MCRA Method

This implementation utilizes the MCRA (Minimum Controlled Recursive Averaging) method for noise power spectrum estimation to accomplish speech enhancement objectives. The approach involves analyzing noise signal characteristics to improve speech signal quality through spectral processing. Key algorithmic steps include: 1) Voice activity detection using spectral minima tracking, 2) Recursive averaging of noise power during non-speech segments, 3) Spectral subtraction for speech enhancement. The MCRA algorithm enables accurate noise power spectrum estimation by maintaining separate update mechanisms for speech and noise periods, allowing precise spectral subtraction parameters. When implementing this method in code, developers typically employ frame-based processing with overlap-add techniques, using FFT for spectral analysis and IFFT for signal reconstruction. This methodology enhances speech clarity and intelligibility while preserving critical phonetic information through controlled noise reduction in the frequency domain.