Create extract_features.py

extract_features.py

@@ -0,0 +1,33 @@
+import numpy as np
+import soundfile
+import librosa
+
+def extract_feature(file_name, **kwargs):
+    
+    chroma = kwargs.get("chroma")
+    contrast = kwargs.get("contrast")
+    mfcc = kwargs.get("mfcc")
+    mel = kwargs.get("mel")
+    tonnetz = kwargs.get("tonnetz")
+    
+    with soundfile.SoundFile(file_name) as audio_clip:
+        X = audio_clip.read(dtype="float32")
+        sound_fourier = np.abs(librosa.stft(X))   # Conducting short time fourier transform of audio clip
+        result = np.array([])
+        
+        if mfcc:
+            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=audio_clip.samplerate, n_mfcc=40).T, axis=0)
+            result = np.hstack((result, mfccs))
+        if chroma:
+            chroma = np.mean(librosa.feature.chroma_stft(S=sound_fourier, sr=audio_clip.samplerate).T, axis=0)
+            result = np.hstack((result, chroma))
+        if mel:
+            mel = np.mean(librosa.feature.melspectrogram(X, sr=audio_clip.samplerate).T, axis=0)
+            result = np.hstack((result, mel))
+        if contrast:
+            contrast = np.mean(librosa.feature.spectral_contrast(S=sound_fourier, sr=audio_clip.samplerate).T, axis=0)
+            result = np.hstack((result, contrast))
+        if tonnetz:
+            tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=audio_clip.samplerate).T, axis=0)
+            result = np.hstack((result, tonnetz))
+    return result