Audio Mezzanine

Scope

Testing CTA-5003 8.2 Sequential Track Playback.
- Optimized for testing section 8.2.5 requirements.
- May be extended in future to also serve as base for other tests.

Reference audio mezzanine files generated using Python script (audiomezz.py).

Can be identically reproduced in the observation framework using the same parametrization of the random noise generation function.
- Samples are generated from a uniform distribution using the NumPy PCG-64 BitGenerator.
- By default, a string of the output filename is converted into Unicode code values that are concatenated, then converted to an integer that is used as the seed for the PRNG. A different text string can be provided using the -s or --seed parameter, and will be used instead of the filename.
- For example: seed = 11610111511695494895504611997118 for an output mezzanine file named test_10_2.wav.
Not necessary to supply generated noise files to the test setup, as they can be recreated using a known seed and PRNG.

Bandlimited (lowpass) version of a pseudo random noise sequence.
Replicated into the left and right channels, with other channels filled with digital silence.

Disregarding the band limitation, each sample is a unique sequence of random values that, with high probability, does not appear anywhere else in the test vector.
Given a sample from an unknown test signal at an unknown offset, it is possible to identify exactly where that sample was located in the signal.

Modern codecs employ techniques that, for higher frequencies, do not attempt to replicate the waveform but rather just the energies.
The identification technique is dependent on the waveform, so band limiting the signal maximizes the signal that is faithfully reproduced from the codec.

Maximizes the probability that the waveform is reproduced faithfully, circumventing signal processing that virtualizes surround or height channels.

With a sample defined as 1024 contiguous PCM audio samples from the signal.

A sample is checked against every 1024 PCM sample subsequence in the reference: measure of similarity is computed.
Maximum similarity when subsequence in the reference corresponds to the sample.
Very efficient implementation of this algorithm provided by algorithms for cross correlation (typically FFT based).
- Open source Python package SciPy provides implementations of this algorithm.

Recording device under test audio output (e.g. headphones, line-out, S/PDIF) is not always possible.
If playing the signal back from the device under test and recording speaker audio output with a microphone, must assume
- noise added to recorded observation,
- phase shift with regards to reference.
Where direct signal comparison would be challenging with the added noise and phase-shift, identification of samples within the signal via cross correlation is robust against noise in the measurement.