[U] Backup unfinished changes

experiment/test_fft.py

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+import librosa
+import librosa.display
+import matplotlib.pyplot as plt
+import numpy as np
+import parselmouth
+
+import sgs
+
+if __name__ == '__main__':
+    librosa.filters.mel(sr=16000, n_fft=1024, htk=True)
+
+    f = 'Z:/EECS 6414/voice_cnn/test.wav'
+    y, sr = librosa.load(f, sr=16000)
+
+    # 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, n_fft=1024, 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=512, htk=True)
+    mel_spect = librosa.power_to_db(mel_spect, ref=np.max)
+
+    print(len(mel_spect))
+    librosa.display.specshow(mel_spect, y_axis='mel', fmax=8000, x_axis='time', n_fft=1024, hop_length=512)
+    result, freq_array = sgs.api.calculate_feature_classification(parselmouth.Sound(f))
+
+    pitch_array = freq_array[:, 0]
+    # x_len = len(pitch_array) / len(mel_spect)
+    # x = np.arange(len(mel_spect))
+    # y = []
+    # for x in range(len(mel_spect) // 2):
+    #     y.append(float(np.mean(pitch_array[int(x_len * x):int(x_len * (x + 1))])))
+    # print(len(y))
+    x = np.linspace(0, 4.1)
+    print(x)
+    x_len = len(pitch_array) / len(x)
+    y = []
+    for a in range(len(x)):
+        y.append(np.mean(pitch_array[int(x_len * a):int(x_len * (a + 1))]))
+
+    plt.plot(x, y, color='#7bff4f')
+    plt.plot(x, [100] * len(x), color='#7bff4f')
+    plt.yticks([0,100,200,300,400,500,600,700,800,900,1000,1200,1400,1600])
+    plt.title('Mel Spectrogram')
+    plt.colorbar(format='%+2.0f dB')
+    plt.show()
+    plt.clf()