Rnonlin

Rnonlin is a metric that measures the amount of perceptible distortion in a distorted track, when compared to its original 'clean' counter.

It was developed by Moore, Tan and others in the paper:

Predicting the Perceived Quality of Nonlinearly Distorted Music and Speech Signals

I have implemented this metric in software, via Matlab. This software as well as LUFS can be found in the linked folder.

What it essentially does is:

• Model the filtering effects of the outer & middle ear on both track versions,
• Filters both track versions through an ERB based Gammatone filter array, to simulate the basilar membrane
• Creates a weighting coefficient set using the maximum level comparisons for the distorted version for each packet of a 30ms sliding window
• Finds the maximum cross-correlation between both versions of the track
• Does this for lags of -10ms to + 10ms
• weights these normalised maximum cross correlations, so all used coefficients sum to 1
• sums across the 40 filtered versions of the signal per packet
• mean averages per packet

Papers like http://projekter.aau.dk/projekter/files/9852082/07gr1061_Thesis.pdf show that it gives excellent correlation with listener perceived levels of distortion.

I will use this to derive how much perceptual distortion is in a track, which is important as I am trying to stay on the fringe of distortion perceptibility, as not to incur a response derived by the fact that the distortion is loud and present, and more the effect of the effect of the particulars of the distortion in and of itself.

https://onedrive.live.com/redir?resid=563e4881c8b0b60!6782&authkey=!ALzVdJrzlx7dS68&ithint=folder%2c

Just getting this thing up to date with the process

The Tests

So, after I got the listening test calibrated, I started testing in and around Markeaton Street. I have jsut over 21 participants worth of data at the moment. Testing is ongoing, but I aim to start creating a data analysis model soon.

Data Analysis

My current data is very promising in many respect, and not so much in others. Most of my data sets correlate, and there is consistency in the point to point ratio of change, even if there is a clear listener to listener shelving difference. Some tracks have a much wider variation of data than others, but everything looks promising. I have the bare minimum of test data in any case.

Writing the Report

I have started filling out the report, Including a large wadge of the literature review and bits. 

Ongoing

As of next week I will keep testing but will start writing a model of the data set. 

More to come as usual