Update mix_w_noise.py

mix_w_noise.py

@@ -0,0 +1,44 @@
+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