84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
from . import blocks, convolve_each, gen_filters, cascades, bq_run, toLK
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import numpy as np
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import matplotlib.pyplot as plt
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def BS1770_3(s, srate, filters=None, window=0.4, overlap=0.75, gate=10, absolute_gate=70, detail=False):
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if filters is None:
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filters = gen_filters(cascades['1770'], srate)
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sf = np.copy(s)
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for f in filters:
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if len(f) is 2: # dumb but effective
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sf = bq_run(f, sf)
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else:
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sf = convolve_each(sf, f, 'same')
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stepsize = round(window*srate*(1 - overlap))
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blocksize = int(stepsize/(1 - overlap))
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means = np.array([
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np.sum(np.mean(b**2, axis=0)) for b in blocks(sf, stepsize, blocksize)
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])
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LKs = toLK(means)
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truths = LKs > -absolute_gate
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LKs_g70 = LKs[truths]
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means_g70 = means[truths]
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avg_g70 = np.average(means_g70)
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threshold = toLK(avg_g70) - gate
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means_g10 = means[LKs_g70 > threshold]
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avg_g10 = np.average(means_g10)
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if detail is False:
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return toLK(avg_g10)
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else:
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return toLK(avg_g10), toLK(avg_g70), LKs, threshold
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def BS_plot(ys, g10=None, g70=None, threshold=None, fig=None, ax=None):
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if g10:
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center = np.round(g10)
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bins = np.arange(center - 10, center + 10.01, 0.25)
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else:
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bins = np.arange(-70, 0.1, 1)
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if fig is None:
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fig = plt.figure()
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if ax is None:
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ax = fig.gca()
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if False: # histogram
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ax.hist(ys, bins=bins, normed=True, facecolor='g', alpha=0.5)
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ax.xlim(bins[0], bins[-1])
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ax.ylim(0, 1)
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ax.grid(True, 'both')
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ax.xlabel('loudness (LKFS)')
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ax.ylabel('probability')
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fig.set_size_inches(10,4)
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show()
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xs = np.arange(len(ys))
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#ax.plot(xs, ys, color='#066ACF', linestyle=':', marker='d', markersize=2)
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ax.plot(xs, ys, color='#1459E0')
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ax.set_xlim(xs[0], xs[-1])
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ax.set_ylim(-70, 0)
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ax.grid(True, 'both', 'y')
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ax.set_xlabel('bin')
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ax.set_ylabel('loudness (LKFS)')
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fig.set_size_inches(12,5)
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#_, _, ymin, _ = ax.axis()
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if threshold:
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ax.axhspan(-70, threshold, facecolor='r', alpha=1/5)
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if g10:
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ax.axhline(g10, color='g')
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if g70:
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ax.axhline(g70, color='0.3')
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return fig, ax
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def normalize(s, srate):
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"""performs BS.1770-3 normalization and returns inverted gain."""
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db = BS1770_3(s, srate)
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rms = 10**(db/20)
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return s/rms, rms
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