cutcutcodec.core.filter.audio.resample.sinc_continuous
- cutcutcodec.core.filter.audio.resample.sinc_continuous(frame: FrameAudio, timestamp: Fraction, rate: int, samples: int, *, win: int = 256) FrameAudio[source]
Resample the signal by the exact continuous function.
It process in two steps: 1) Discrete to continuous signal. Convolute by a cardinal sinus. 2) Continus to dicrete. Eval the continuous signal on some points.
There is no anti-aliasing filter for downsampeling. We assume that the signal doese not contains any frequecies other rate/2. The temporal and spacial complexity is o(samples*win).
Parameters
- framecutcutcodec.core.classes.fream_audio.FrameAudio
The reference frame to resample.
- timestampFraction
The starting time the new resampled frame. For more information, see
cutcutcodec.core.classes.stream_audio.StreamAudio.snapshot.- rateint
The new sample rate. For more information, see
cutcutcodec.core.classes.stream_audio.StreamAudio.snapshot.- samplesint
The number of samples in the new resampled frame. For more information, see
cutcutcodec.core.classes.stream_audio.StreamAudio.snapshot.- winint, optinal
The maximum half width of the sliding windows. It correspond to the number of samples in the source frame that contribute to compute one sample in the destination frame. Bigger it is, slowler is the computation o(n), but more is accurate. Has to be odd number.
Returns
- new_framecutcutcodec.core.classes.fream_audio.FrameAudio
The resampled frame.
Examples
>>> from fractions import Fraction >>> import torch >>> from cutcutcodec.core.filter.audio.resample import sinc_continuous >>> from cutcutcodec.core.classes.frame_audio import FrameAudio >>> frame = FrameAudio(1, 8000, "mono", torch.empty(1, 512)) >>> frame[:, :] = torch.sin(2*torch.pi*3960*frame.timestamps).reshape(1, -1) # 1% to shannon >>> frame[:, :] *= torch.blackman_window(frame.samples, periodic=False).reshape(1, -1) >>> new_frame = sinc_continuous(frame, 1-Fraction(16, 8000), 96000, 96000*544//8000) >>> >>> torch.round(frame, decimals=3) FrameAudio(1, 8000, 'mono', [[-0. , 0. , -0. , 0. , -0. , 0. , -0. , 0. , -0. , 0. , -0. , 0.001, -0.001, 0.001, -0.001, 0.001, -0.002, 0.002, -0.002, 0.003, -0.003, 0.004, -0.004, 0.005, -0.006, 0.006, -0.007, 0.008, -0.009, 0.009, -0.01 , 0.011, -0.012, 0.013, -0.015, 0.016, -0.017, 0.018, -0.02 , 0.021, -0.022, 0.024, -0.025, 0.027, -0.028, 0.03 , -0.032, 0.033, -0.035, 0.037, -0.038, 0.04 , -0.042, 0.043, -0.045, 0.047, -0.048, 0.05 , -0.052, 0.053, -0.055, 0.056, -0.058, 0.059, -0.06 , 0.062, -0.063, 0.064, -0.065, 0.066, -0.066, 0.067, -0.067, 0.068, -0.068, 0.068, -0.068, 0.068, -0.067, 0.067, -0.066, 0.065, -0.064, 0.062, -0.061, 0.059, -0.057, 0.054, -0.052, 0.049, -0.046, 0.042, -0.039, 0.035, -0.031, 0.026, -0.022, 0.017, -0.011, 0.006, -0. , -0.006, 0.012, -0.019, 0.026, -0.034, 0.041, -0.049, 0.057, -0.065, 0.074, -0.083, 0.091, -0.101, 0.111, -0.121, 0.13 , -0.141, 0.152, -0.161, 0.173, -0.183, 0.195, -0.206, 0.218, -0.229, 0.24 , -0.252, 0.263, -0.275, 0.286, -0.298, 0.31 , -0.321, 0.332, -0.343, 0.355, -0.366, 0.377, -0.388, 0.398, -0.408, 0.418, -0.428, 0.438, -0.447, 0.456, -0.464, 0.472, -0.48 , 0.487, -0.493, 0.5 , -0.505, 0.511, -0.515, 0.519, -0.523, 0.526, -0.528, 0.53 , -0.531, 0.531, -0.531, 0.529, -0.528, 0.526, -0.522, 0.518, -0.513, 0.508, -0.501, 0.494, -0.488, 0.478, -0.469, 0.458, -0.448, 0.437, -0.423, 0.41 , -0.395, 0.381, -0.365, 0.35 , -0.333, 0.314, -0.296, 0.276, -0.257, 0.235, -0.214, 0.193, -0.17 , 0.148, -0.124, 0.101, -0.075, 0.051, -0.026, 0. , 0.025, -0.054, 0.08 , -0.106, 0.133, -0.16 , 0.188, -0.215, 0.245, -0.272, 0.299, -0.327, 0.354, -0.384, 0.408, -0.438, 0.464, -0.49 , 0.517, -0.542, 0.57 , -0.594, 0.62 , -0.643, 0.666, -0.69 , 0.712, -0.736, 0.756, -0.777, 0.796, -0.815, 0.835, -0.851, 0.868, -0.884, 0.898, -0.912, 0.924, -0.937, 0.948, -0.958, 0.966, -0.974, 0.981, -0.987, 0.991, -0.995, 0.997, -0.998, 0.998, -0.997, 0.995, -0.992, 0.988, -0.982, 0.976, -0.969, 0.959, -0.95 , 0.939, -0.928, 0.915, -0.901, 0.886, -0.87 , 0.854, -0.835, 0.819, -0.8 , 0.778, -0.758, 0.736, -0.715, 0.69 , -0.667, 0.643, -0.618, 0.593, -0.567, 0.541, -0.512, 0.486, -0.459, 0.43 , -0.403, 0.372, -0.345, 0.315, -0.287, 0.259, -0.229, 0.201, -0.17 , 0.142, -0.112, 0.085, -0.057, 0.027, 0. , -0.028, 0.055, -0.08 , 0.108, -0.133, 0.161, -0.185, 0.21 , -0.233, 0.255, -0.28 , 0.3 , -0.323, 0.343, -0.364, 0.382, -0.4 , 0.42 , -0.436, 0.453, -0.467, 0.484, -0.496, 0.508, -0.522, 0.532, -0.543, 0.552, -0.561, 0.57 , -0.576, 0.583, -0.588, 0.593, -0.596, 0.599, -0.602, 0.603, -0.604, 0.603, -0.603, 0.601, -0.599, 0.596, -0.592, 0.588, -0.583, 0.577, -0.571, 0.564, -0.557, 0.549, -0.54 , 0.532, -0.522, 0.512, -0.502, 0.492, -0.481, 0.469, -0.458, 0.446, -0.434, 0.422, -0.409, 0.397, -0.383, 0.37 , -0.358, 0.344, -0.331, 0.317, -0.304, 0.291, -0.277, 0.265, -0.251, 0.238, -0.224, 0.212, -0.199, 0.187, -0.174, 0.161, -0.15 , 0.137, -0.126, 0.115, -0.104, 0.093, -0.082, 0.072, -0.062, 0.052, -0.043, 0.033, -0.025, 0.016, -0.008, -0. , 0.007, -0.014, 0.022, -0.028, 0.034, -0.04 , 0.046, -0.051, 0.055, -0.06 , 0.064, -0.068, 0.072, -0.076, 0.079, -0.081, 0.084, -0.086, 0.088, -0.089, 0.091, -0.092, 0.093, -0.094, 0.094, -0.094, 0.094, -0.094, 0.094, -0.093, 0.093, -0.092, 0.091, -0.09 , 0.088, -0.087, 0.086, -0.084, 0.082, -0.081, 0.079, -0.077, 0.075, -0.073, 0.071, -0.069, 0.066, -0.064, 0.062, -0.06 , 0.058, -0.055, 0.053, -0.051, 0.049, -0.047, 0.044, -0.042, 0.04 , -0.038, 0.036, -0.034, 0.032, -0.03 , 0.028, -0.027, 0.025, -0.023, 0.022, -0.02 , 0.019, -0.017, 0.016, -0.015, 0.013, -0.012, 0.011, -0.01 , 0.009, -0.008, 0.007, -0.006, 0.006, -0.005, 0.004, -0.004, 0.003, -0.003, 0.002, -0.002, 0.002, -0.001, 0.001, -0.001, 0.001, -0. , 0. , -0. , 0. , 0. , -0. , 0. , -0. , 0. , -0. , 0. , -0. , 0. , -0. , 0. , 0. ]]) >>> middle = new_frame.samples//2 >>> torch.round(new_frame[:, middle-32:middle+32], decimals=3).numpy() array([[ 0.588, 0.359, 0.109, -0.151, -0.398, -0.619, -0.8 , -0.927, -0.992, -0.99 , -0.923, -0.793, -0.612, -0.39 , -0.139, 0.118, 0.371, 0.595, 0.781, 0.915, 0.988, 0.995, 0.935, 0.813, 0.635, 0.417, 0.172, -0.088, -0.339, -0.571, -0.761, -0.901, -0.982, -0.997, -0.946, -0.83 , -0.66 , -0.444, -0.201, 0.056, 0.312, 0.544, 0.742, 0.889, 0.976, 0.999, 0.956, 0.848, 0.683, 0.474, 0.231, -0.025, -0.28 , -0.518, -0.719, -0.873, -0.969, -1. , -0.964, -0.864, -0.707, -0.501, -0.263, -0.009]], dtype=float32) >>> >>> # import matplotlib.pyplot as plt >>> # plt.xlabel("time (s)"); plt.ylabel("magnitude") >>> # plt.plot(frame.timestamps.numpy(), frame.transpose(0, 1).numpy(), marker="o") >>> # plt.plot(new_frame.timestamps.numpy(), new_frame.transpose(0, 1).numpy(), marker="o") >>> # plt.show() >>>