Create test_fft.py

src/test_fft.py

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+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+
+if __name__ == '__main__':
+    y, sr = librosa.load('../test.wav')
+
+    # Plot waveform
+    plt.plot(y)
+    plt.title('Signal')
+    plt.xlabel('Time (samples)')
+    plt.ylabel('Amplitude')
+    plt.show()
+    plt.clf()
+
+    # Plot frequency domain graph at a single time
+    n_fft = 2048
+    ft = np.abs(librosa.stft(y[:n_fft], hop_length=n_fft + 1))
+
+    plt.plot(ft)
+    plt.title('Spectrum')
+    plt.xlabel('Frequency Bin')
+    plt.ylabel('Amplitude')
+    plt.show()
+    plt.clf()
+
+    # Plot spectrogram
+    spec = np.abs(librosa.stft(y, hop_length=512))
+    spec = librosa.amplitude_to_db(spec, ref=np.max)
+
+    librosa.display.specshow(spec, sr=sr, x_axis='time', y_axis='log')
+    plt.colorbar(format='%+2.0f dB')
+    plt.title('Spectrogram')
+    plt.show()
+    plt.clf()
+
+    # Mel transform
+    mel_spect = librosa.feature.melspectrogram(y=y, sr=sr, n_fft=2048, hop_length=1024)
+    mel_spect = librosa.power_to_db(mel_spect, ref=np.max)
+
+    librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time')
+    plt.title('Mel Spectrogram')
+    plt.colorbar(format='%+2.0f dB')
+    plt.show()
+    plt.clf()