analysis.py

import os
import pandas as pd
import numpy as np
import librosa


music_list = os.listdir('music/getup/')
print(music_list)

feature_list = ['mfcc', 'chroma_stft', 'chroma_cqt', 'chroma_cens', 'melspectrogram', 'rms', 'spectral_centroid', 'spectral_bandwidth', 'spectral_flatness',
                 'spectral_rolloff', 'tonnetz', 'zero_crossing_rate', 'tempogram', 'fourier_tempogram']


def analyze(title):
    # data = os.listdir('music/getup/%s'%(title))

    df = pd.DataFrame()
    
    # for i in range(len(data)):
    y, sr = librosa.load('music/getup/%s'%(title))

    # FEATURE EXTRACTION STARTS FROM HERE
    mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=12)
    chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
    chroma_cqt = librosa.feature.chroma_cqt(y=y, sr=sr)
    chroma_cens = librosa.feature.chroma_cens(y=y, sr=sr)
    melspectrogram = librosa.feature.melspectrogram(y=y, sr=sr)
    rms = librosa.feature.rms(y=y)
    spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)
    spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y, sr=sr)
    spectral_flatness = librosa.feature.spectral_flatness(y=y)
    spectral_rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
    # poly_features = librosa.feature.poly_features(y=y, sr=sr)
    tonnetz = librosa.feature.tonnetz(y=y, sr=sr)
    zero_crossing_rate = librosa.feature.zero_crossing_rate(y=y)

    tempogram = librosa.feature.tempogram(y=y, sr=sr)
    fourier_tempogram = librosa.feature.fourier_tempogram(y=y, sr=sr)

    df = df.append(pd.DataFrame([[mfcc, chroma_stft, chroma_cqt, chroma_cens, melspectrogram, 
                rms, spectral_centroid, spectral_bandwidth, spectral_flatness,
                spectral_rolloff, tonnetz, zero_crossing_rate, tempogram, fourier_tempogram]]), 
                ignore_index=True)
    
    df.columns = ['mfcc', 'chroma_stft', 'chroma_cqt', 'chroma_cens', 'melspectrogram',
                'rms', 'spectral_centroid', 'spectral_bandwidth', 'spectral_flatness',
                'spectral_rolloff', 'tonnetz', 'zero_crossing_rate', 'tempogram', 'fourier_tempogram']


    df.to_excel('./musicdata/%s.xlsx'%(title))
    return df

        

def result():
    for i in range(len(music_list)):
        analyze(music_list[i])



result()