feature_extraction.py

#!/usr/bin/env python3

# Imports

import librosa
import numpy as np

import os
import csv

# The Features to be Extracted as headers

header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate'
for i in range(1, 21):
    header += f' mfcc{i}'
header += ' label'
header = header.split()

# Creating a CSV File
csv_path = input('Enter the path for CSV file containing the features: ')
file = open(csv_path, 'w', newline='')
with file:
    writer = csv.writer(file)
    writer.writerow(header)

# The Genres in Dataset
genres = 'blues classical country disco hiphop jazz metal pop reggae rock'.split()
print("Enter the path of where you downloaded the Database"
      "Example - C:/Users/<user-name>/Downloads/GTZAN/")

database_path = input("Path: ")
for g in genres:
    # Traversing through various genres in the Dataset
    # Feed the complete path of the GTZAN folder
    for filename in os.listdir(database_path + '/' + g):

        # Traversing through various songs in a particular genre.

        songname = database_path + '/' + g + '/' + filename
        # Using LibRosa to determine the features
        y, sr = librosa.load(songname, mono=True, duration=30)
        chroma_stft = librosa.feature.chroma_stft(y=y, sr=sr)
        rmse = librosa.feature.rms(y=y)
        spec_cent = librosa.feature.spectral_centroid(y=y, sr=sr)
        spec_bw = librosa.feature.spectral_bandwidth(y=y, sr=sr)
        rolloff = librosa.feature.spectral_rolloff(y=y, sr=sr)
        zcr = librosa.feature.zero_crossing_rate(y)
        mfcc = librosa.feature.mfcc(y=y, sr=sr)
        to_append = f'{filename} {np.mean(chroma_stft)} {np.mean(rmse)} {np.mean(spec_cent)} {np.mean(spec_bw)} {np.mean(rolloff)} {np.mean(zcr)}'
        for e in mfcc:
            to_append += f' {np.mean(e)}'
        to_append += f' {g}'

        # Writing all the information in the CSV
        file = open(csv_path, 'a', newline='')
        with file:
            writer = csv.writer(file)
            writer.writerow(to_append.split())