MSU Noise Estimator 0.1

MSU Noise Estimator 0.1 is an ideal tool for anyone who has to work with audio a great deal. It is actually a free MSU VQM plugin for noise level estimation. It includes implementation of three most popular algorithms for measurement of noise level. By results of job of the metrics the plot of frame-accurate value of noise level is constructed. Final value of the metrics is average arithmetic of all frame-accurate values.

Algorithms:

• MAD - For each frame do HAAR wavelet decomposition. Than evaluate median of HH-component's absolute values. Final value of the metrics is the normalized median.
• Block-Based - Frames are tessellated into a number of 8x8 blocks. Standard deviations of intensity (measures of intensity variation) are computed for all the blocks and sorted. The block with the smallest standard deviation has the least change of intensity. The smaller the standard deviation, the smother the block. The intensity variation of a smooth block may be due to noise, in which the standard deviation of the block is close to that of the Gaussian noise added. Normalized average arithmetic values of 30 % of all blocks with the least values grows is the final value of the metric.
• Spatio-Temporal Gradients - For each frame is doing wavelet decomposition. computing temporal and spatial histograms. The initial estimation of noise level is defined by value at which temporal or spatial histogram achieves the maximal value. The decision of whether to use the spatial or temporal histogram is based on the deviation of the histogram from the Rayleigh distribution. Then this estimation is corrected, using Kolmogorov-Smirnoff test. The normalized corrected estimation is the final value of the metric.

Major Features:

1. Completely free!
2. Offers three most popular algorithms for measurement of noise level
   • MAD
   • Block-Based
   • Spatio-Temporal Gradients
     
3. Very easy to use
   

Requirements: Pentium 700 Mhz