docs/ensemble.md

Ensemble usage

Repository contains ensemble.py script which can be used to ensemble results of different algorithms.

Arguments:

• --files - Path to all audio-files to ensemble
• --type - Method to do ensemble. One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft. Default: avg_wave.
• --weights - Weights to create ensemble. Number of weights must be equal to number of files
• --output - Path to wav file where ensemble result will be stored (Default: res.wav)

Example:

ensemble.py --files ./results_tracks/vocals1.wav ./results_tracks/vocals2.wav --weights 2 1 --type max_fft --output out.wav

Ensemble types:

• avg_wave - ensemble on 1D variant, find average for every sample of waveform independently
• median_wave - ensemble on 1D variant, find median value for every sample of waveform independently
• min_wave - ensemble on 1D variant, find minimum absolute value for every sample of waveform independently
• max_wave - ensemble on 1D variant, find maximum absolute value for every sample of waveform independently
• avg_fft - ensemble on spectrogram (Short-time Fourier transform (STFT), 2D variant), find average for every pixel of spectrogram independently. After averaging use inverse STFT to obtain original 1D-waveform back.
• median_fft - the same as avg_fft but use median instead of mean (only useful for ensembling of 3 or more sources).
• min_fft - the same as avg_fft but use minimum function instead of mean (reduce aggressiveness).
• max_fft - the same as avg_fft but use maximum function instead of mean (the most aggressive).

Notes

• min_fft can be used to do more conservative ensemble - it will reduce influence of more aggressive models.
• It's better to ensemble models which are of equal quality - in this case it will give gain. If one of model is bad - it will reduce overall quality.
• In my experiments avg_wave was always better or equal in SDR score comparing with other methods.