MBHM / src /dcn.py
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import numpy as np
from scipy.fft import dct
def dcn(raw_data, target_rate=24000):
data_std = np.std(raw_data)
data_mean = np.mean(raw_data)
data = (raw_data - data_mean) / data_std
data = dct(data)
if len(data) < target_rate:
data = np.pad(data, (0, target_rate - len(data)))
else:
data = data[:target_rate]
data_energy = np.sum(data ** 2)
data = data * np.sqrt(target_rate / data_energy) / 100
data = data.astype(np.float32)
return data
if __name__ == '__main__':
rand_data = np.random.rand(123456)
print(dcn(rand_data).shape)