This repository contains all the python experiments I made based on the IEEE research paper Antiderivative Antialiasing for Arbitrary Waveform Generation
The paper provided an algorithm, some results and some matlab demo code which you can find here
This work was presented at the Audio Developer Conference 2023 alongside its C++ implementation for real-time here.
The code contained in here is certainly not production ready, but I made what I could to understand, replicate, and further adapt the algorithm to a real-time scenario.
Keep in mind that I'm not a DSP specialist, if you find something weird or buggy in my code don't hesitate to tell it. Also this repository is not dedicated to explain the algorithm in any case.
This repository contains 3 mains parts :
matlab: contains the Matlab code of the paper demo, slightly modified to run with Octavepython: contains the different versions of the algorithm and some tools to analyze the results (metrics, graphs...)python/legacy.py: contains some iterations of my work when adapting the algorithm. It's only provided for R&D legacy and should not be considered reliable
- Matlab is no longer required to compute SNR. Both SNR and SINAD computations are working as expected and in full python code
- Following SINAD fixes, I changed the mipmap transition thresholds, algorithm should be a little slower
- The cross-fading is no longer using this weird frequency-based strategy I presented at ADC23, now using a more classic time-based strategy
The following tools are required :
libsamplerate: for mipmapping resamplinglibsndfile: for audio exporting
On Ubuntu you can install libsamplerate and libsndfile with the following command:
apt-get install -y libsamplerate0 libsndfile1After that you will need to install the python requirements :
pip install -r requirements.txtI provide a main python script that can performs three tasks, on different version of both the ADAA algorithm, and its alternatives (lerp + oversampling) :
- Metrics computation (SNR and SINAD)
- Sweep test spectrogram plot
- Power spectral density plot
Some values still needs to be modified manually in the main.py file depending on your use case:
DURATION_S: The duration of generated audio, might lead to high ram usage if too highFREQS: A list of frequencies to generate for (only in psd/metrics modes)ALGOS_OPTIONS: A list of all the algorithm to testNUM_PROCESS: The number of parallel process, maxed out to 20, mined out to you ncpusSAMPLERATE
For the metrics mode use the following options :
python python/main.py metrics [--export {snr,sinad,both,none}] [--export-dir EXPORT_DIR] [--export-audio] [--export-phase]You'd usually want to add all the frequencies you want to test in FREQS.
The script will write the metrics in CSV files.
For the metrics mode use the following options :
python python/main.py sweep [--export-dir EXPORT_DIR] [--export-audio] [--export-phase]This will automatically generate a sweep test from 20Hz to Nyquist and plot its spectrogram.
This mode will not read the FREQS variable.
I suggest a duration of 5s to have a good enough resolution in the spectrogram.
For the psd mode use the following options :
python python/main.py psd [--export-dir EXPORT_DIR] [--export-audio] [--export-phase]This will use a matplotlib graph to display the psd values for each test, and a final graph with all the waveforms on the same graphs.
This mode requires FREQS to contains a single value
As mentioned above, this is an experimentation repo, not a tool designed for advanced use or anything like it.
I don't plan to make modifications to make it a user-friendly demo tool. However I'm open to suggestions in order to help further researchs such as :
- Improvements on the argparser to allow passing frequencies and/or other parameters
Metrics improvements/fixesDONE- Improvement/Changes in the algorithm
If you wan't to discuss about it you can open an issue or you can find me on :