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import numpy as np |
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def a_weight(fs, n_fft, min_db=-80.0): |
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freq = np.linspace(0, fs // 2, n_fft // 2 + 1) |
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freq_sq = np.power(freq, 2) |
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freq_sq[0] = 1.0 |
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weight = 2.0 + 20.0 * (2 * np.log10(12194) + 2 * np.log10(freq_sq) |
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- np.log10(freq_sq + 12194 ** 2) |
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- np.log10(freq_sq + 20.6 ** 2) |
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- 0.5 * np.log10(freq_sq + 107.7 ** 2) |
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- 0.5 * np.log10(freq_sq + 737.9 ** 2)) |
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weight = np.maximum(weight, min_db) |
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return weight |
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def compute_gain(sound, fs, min_db=-80.0, mode="A_weighting"): |
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if fs == 16000: |
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n_fft = 2048 |
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elif fs == 44100: |
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n_fft = 4096 |
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else: |
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raise Exception("Invalid fs {}".format(fs)) |
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stride = n_fft // 2 |
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gain = [] |
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for i in range(0, len(sound) - n_fft + 1, stride): |
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if mode == "RMSE": |
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g = np.mean(sound[i: i + n_fft] ** 2) |
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elif mode == "A_weighting": |
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spec = np.fft.rfft(np.hanning(n_fft + 1)[:-1] * sound[i: i + n_fft]) |
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power_spec = np.abs(spec) ** 2 |
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a_weighted_spec = power_spec * np.power(10, a_weight(fs, n_fft) / 10) |
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g = np.sum(a_weighted_spec) |
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else: |
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raise Exception("Invalid mode {}".format(mode)) |
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gain.append(g) |
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gain = np.array(gain) |
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gain = np.maximum(gain, np.power(10, min_db / 10)) |
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gain_db = 10 * np.log10(gain) |
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return gain_db |
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def mix(sound1, sound2, r, fs): |
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gain1 = np.max(compute_gain(sound1, fs)) |
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gain2 = np.max(compute_gain(sound2, fs)) |
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t = 1.0 / (1 + np.power(10, (gain1 - gain2) / 20.) * (1 - r) / r) |
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sound = ((sound1 * t + sound2 * (1 - t)) / np.sqrt(t ** 2 + (1 - t) ** 2)) |
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return sound |
