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### Ensemble usage |
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Repository contains `ensemble.py` script which can be used to ensemble results of different algorithms. |
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Arguments: |
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* `--files` - Path to all audio-files to ensemble |
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* `--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. |
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* `--weights` - Weights to create ensemble. Number of weights must be equal to number of files |
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* `--output` - Path to wav file where ensemble result will be stored (Default: res.wav) |
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Example: |
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``` |
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ensemble.py --files ./results_tracks/vocals1.wav ./results_tracks/vocals2.wav --weights 2 1 --type max_fft --output out.wav |
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``` |
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### Ensemble types: |
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* `avg_wave` - ensemble on 1D variant, find average for every sample of waveform independently |
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* `median_wave` - ensemble on 1D variant, find median value for every sample of waveform independently |
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* `min_wave` - ensemble on 1D variant, find minimum absolute value for every sample of waveform independently |
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* `max_wave` - ensemble on 1D variant, find maximum absolute value for every sample of waveform independently |
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* `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. |
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* `median_fft` - the same as avg_fft but use median instead of mean (only useful for ensembling of 3 or more sources). |
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* `min_fft` - the same as avg_fft but use minimum function instead of mean (reduce aggressiveness). |
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* `max_fft` - the same as avg_fft but use maximum function instead of mean (the most aggressive). |
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### Notes |
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* `min_fft` can be used to do more conservative ensemble - it will reduce influence of more aggressive models. |
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* 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. |
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* In my experiments `avg_wave` was always better or equal in SDR score comparing with other methods. |
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