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import numba as nb |
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import numpy as np |
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@nb.njit() |
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def find_nearest_stft_bin(f0_, freqs): |
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freqs = np.expand_dims(freqs, 0) |
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f0_ = np.expand_dims(f0_, 1) |
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return np.abs(freqs - f0_).argmin() |
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@nb.njit() |
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def get_med_curve(f0, step_size=20): |
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v_begin = -1 |
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v_end = -1 |
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x_med_curve = [] |
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y_med_curve = [] |
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T = len(f0) |
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for i in range(T): |
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if f0[i] >= 50 and i < T - 1: |
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if v_begin == -1: |
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v_begin = i |
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v_end = i |
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else: |
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if v_end != -1: |
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if v_end - v_begin > 3: |
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for j in range(v_begin, v_end + 1 - step_size, step_size): |
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frag_med = np.median(f0[j:j + step_size]) |
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x_med_curve.append(j) |
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y_med_curve.append(frag_med) |
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x_med_curve.append(v_end) |
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y_med_curve.append(np.median(f0[v_end - step_size:v_end + 1])) |
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v_end = v_begin = -1 |
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x_med_curve = [0] + x_med_curve + [T] |
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x_med_curve = np.array(x_med_curve) |
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y_med_curve = [y_med_curve[0]] + y_med_curve + [y_med_curve[-1]] |
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y_med_curve = np.array(y_med_curve) |
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return x_med_curve, y_med_curve |
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@nb.njit() |
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def clean_short_v_frag(f0): |
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v_begin = -1 |
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T = len(f0) |
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uv = np.zeros_like(f0).astype(np.bool_) |
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for i in range(T): |
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if f0[i] >= 1e-4 and i < T - 1: |
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if v_begin == -1: |
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v_begin = i |
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else: |
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if v_begin != -1: |
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v_end = i if f0[i] >= 1e-4 else i - 1 |
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if v_end - v_begin + 1 < 3: |
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uv[v_begin:v_end + 1] = 1 |
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v_begin = -1 |
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return uv |
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@nb.njit() |
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def find_best_f0_using_har_energy(spec, pitches, freqs, hars, hars_mhalf, f0_min, f0_max): |
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re = np.zeros_like(spec) |
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T = len(spec) |
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for i in range(T): |
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spec_i = spec[i] |
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for j, f0_j in enumerate(pitches[i]): |
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if f0_j == 0 or f0_j < f0_min[i] or f0_j > f0_max[i]: |
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continue |
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mask = np.zeros((10000,)) |
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mask_mhalf = np.zeros((10000,)) |
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for mul in hars: |
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b = find_nearest_stft_bin(np.array((f0_j * mul,)), freqs) |
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for delta in range(-1, 2): |
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mask[b + delta] = 1 |
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for mul in hars_mhalf: |
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b_mhalf = find_nearest_stft_bin(np.array((f0_j * (mul - 0.5),)), freqs) |
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for delta in range(-1, 2): |
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mask_mhalf[b_mhalf + delta] = 1 |
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mask = mask[:len(spec_i)] |
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mask_mhalf = mask_mhalf[:len(spec_i)] |
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energy = (np.exp(spec_i) * mask).sum() / mask.sum() |
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energy_mhalf = (np.exp(spec_i) * mask_mhalf).sum() / mask_mhalf.sum() |
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re[i, j] = energy / energy_mhalf |
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f0_2d_mask = 10000 * (re > 2) + 20000 * (re > 3) + np.expand_dims(np.arange(re.shape[1])[::-1], 0) |
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f0_idx = np.zeros((T,), dtype=np.int_) |
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for i in range(T): |
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f0_idx[i] = f0_2d_mask[i].argmax() |
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uv = re.sum(-1) == 0 |
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f0 = np.zeros((T,)) |
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for i in range(T): |
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f0[i] = pitches[i, f0_idx[i]] |
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f0 = f0 * (1 - uv) |
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uv = clean_short_v_frag(f0) |
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f0[uv] = 0 |
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x_med_curve, y_med_curve = get_med_curve(f0) |
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re = re * (re > 1.5) |
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return re, f0, x_med_curve, y_med_curve |
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