delete

extract_features.py

@@ -1,33 +0,0 @@
-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