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| import numpy as np | |
| from scipy.io.wavfile import read, write | |
| import librosa | |
| def mix_audio(signal_file, noise_file, snr): | |
| # if the audio is longer than the noise | |
| # play the noise in repeat for the duration of the audio | |
| signal_samples,s_rate= librosa.load(signal_file, sr=16000) | |
| noise_samples, s_rate= librosa.load(noise_file, sr=16000) | |
| print(signal_samples.shape, noise_samples.shape) | |
| signal=np.array(signal_samples,dtype=float) | |
| noise= np.array(noise_samples,dtype=float) | |
| noise = noise[np.arange(len(signal)) % len(noise)] | |
| # if the audio is shorter than the noi | |
| # this is important if loading resulted in | |
| # uint8 or uint16 types, because it would cause overflow | |
| # when squaring and calculating mean | |
| noise = noise.astype(np.float32) | |
| signal = signal.astype(np.float32) | |
| # get the initial energy for reference | |
| signal_energy = np.mean(signal**2) | |
| noise_energy = np.mean(noise**2) | |
| # calculates the gain to be applied to the noise | |
| # to achieve the given SNR | |
| g = np.sqrt(10.0 ** (-snr/10) * signal_energy / noise_energy) | |
| # Assumes signal and noise to be decorrelated | |
| # and calculate (a, b) such that energy of | |
| # a*signal + b*noise matches the energy of the input signal | |
| a = np.sqrt(1 / (1 + g**2)) | |
| b = np.sqrt(g**2 / (1 + g**2)) | |
| print(g, a, b) | |
| monaural= a * signal + b * noise | |
| write('output.wav', 16000, monaural) | |
| # mix the signals | |
| return monaural |