58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
import numpy as np
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from thunderhopper.model import configuration, process_signal
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from thunderhopper.modeltools import load_data
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from IPython import embed
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## SETTINGS:
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# General:
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save_path = '../data/processed/white_noise'
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stages = ['raw', 'filt', 'env', 'log', 'inv', 'conv', 'bi', 'feat']
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sds = [1]
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dur = 60
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# Interactivity:
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reload_saved = False
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# Processing:
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rate = 44100.0
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env_rate = 44100.0
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feat_rate = 44100.0
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sigmas = [0.001, 0.002, 0.004, 0.008, 0.016, 0.032]
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types = [1, -1, 2, -2, 3, -3, 4, -4, 5, -5,
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6, -6, 7, -7, 8, -8, 9, -9, 10, -10]
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config = configuration(env_rate, feat_rate, types=types, sigmas=sigmas)
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config.update({
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'rate_ratio': None,
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'env_fcut': 250,
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'db_ref': 1,
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'inv_fcut': 5,
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'feat_thresh': np.load('../data/kernel_thresholds.npy') * 0.2,
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'feat_fcut': 0.5,
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'label_channels': 0,
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'label_thresh': 0.5,
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})
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## PREPARATION:
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n_samples = int(dur * env_rate)
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rng = np.random.default_rng()
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# PROCESSING:
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for sd in sds:
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print('Processing: SD =', sd)
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# Generate white noise signal:
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noise = rng.normal(loc=0, scale=sd, size=n_samples)
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# Fetch and store representations:
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save = None if save_path is None else save_path + f'_sd-{sd}.npz'
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process_signal(config, stages, signal=noise, rate=rate, save=save)
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# Cross-control:
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if reload_saved:
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data, params = load_data(save, stages, ['songs', 'noise'])
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embed()
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print('Done.')
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