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| import math | |
| import numba as nb | |
| import numpy as np | |
| def stonemask(x, fs, temporal_positions, f0): | |
| refined_f0 = np.copy(f0) | |
| for i in range(len(temporal_positions)): | |
| if f0[i] != 0: | |
| refined_f0[i] = get_refined_f0(x, fs, temporal_positions[i], f0[i]) | |
| if abs(refined_f0[i] - f0[i]) / f0[i] > 0.2: refined_f0[i] = f0[i] | |
| return np.array(refined_f0, dtype=np.float32) | |
| def get_refined_f0(x, fs, current_time, current_f0): | |
| f0_initial = current_f0 | |
| half_window_length = np.ceil(3 * fs / f0_initial / 2) | |
| window_length_in_time = (2 * half_window_length + 1) / fs | |
| base_time = np.arange(-half_window_length, half_window_length + 1) / fs | |
| fft_size = 2 ** math.ceil(math.log((half_window_length * 2 + 1), 2) + 1) | |
| base_time = np.array([float("{0:.4f}".format(elm)) for elm in base_time]) | |
| index_raw = round_matlab((current_time + base_time) * fs) | |
| window_time = ((index_raw - 1) / fs) - current_time | |
| main_window = 0.42 + 0.5 * np.cos(2 * math.pi * window_time / window_length_in_time) + 0.08 * np.cos(4 * math.pi * window_time / window_length_in_time) | |
| index = np.array(np.maximum(1, np.minimum(len(x), index_raw)), dtype=int) | |
| spectrum = np.fft.fft(x[index - 1] * main_window, fft_size) | |
| diff_spectrum = np.fft.fft(x[index - 1] * (-(np.diff(np.r_[0, main_window]) + np.diff(np.r_[main_window, 0])) / 2), fft_size) | |
| power_spectrum = np.abs(spectrum) ** 2 | |
| from sys import float_info | |
| power_spectrum[power_spectrum == 0] = float_info.epsilon | |
| instantaneous_frequency = (np.arange(fft_size) / fft_size * fs) + (np.real(spectrum) * np.imag(diff_spectrum) - np.imag(spectrum) * np.real(diff_spectrum)) / power_spectrum * fs / 2 / math.pi | |
| trim_index = np.array([1, 2]) | |
| index_list_trim = np.array(round_matlab(f0_initial * fft_size / fs * trim_index) + 1, int) | |
| amp_list = np.sqrt(power_spectrum[index_list_trim - 1]) | |
| f0_initial = np.sum(amp_list * instantaneous_frequency[index_list_trim - 1]) / np.sum(amp_list * trim_index) | |
| if f0_initial < 0: return 0 | |
| trim_index = np.array([1, 2, 3, 4, 5, 6]) | |
| index_list_trim = np.array(round_matlab(f0_initial * fft_size / fs * trim_index) + 1, int) | |
| amp_list = np.sqrt(power_spectrum[index_list_trim - 1]) | |
| return np.sum(amp_list * instantaneous_frequency[index_list_trim - 1]) / np.sum(amp_list * trim_index) | |
| def round_matlab(x: np.ndarray) -> np.ndarray: | |
| y = x.copy() | |
| y[x > 0] += 0.5 | |
| y[x <= 0] -= 0.5 | |
| return y |