microsoft audio processing music classification

We’ve split the dataset into training and testing dataset in 80:20 ratio. Splitting the dataset into training and testing dataset X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) In this x is calculated by removing the mean and dividing by the variance.

Music genre Normalizing the dataset scaler = StandardScaler() X = scaler.fit_transform(np.array(data.iloc, dtype = float)) Each integer represents the specific genre. Here, we created a mapping between genres and integers. Now, we’ll encode genres into integers genre_list = data.iloc encoder = LabelEncoder() y = encoder.fit_transform(genre_list) print(y) Preprocessing Dataset Reading a dataset data = pd.read_csv('data.csv') data.head() Dropping unnecessary columns # Dropping unneccesary columns data = data.drop(,axis=1) data.head() We’ve calculated all the features using librosa package and has created a dataset with the data.csv file name and has inserted all the feature values of given music in given headers. header = 'filename chroma_stft rmse spectral_centroid spectral_bandwidth rolloff zero_crossing_rate' for i in range(1, 21): header += f' mfcc' file = open('data.csv', 'a', newline='') with file: writer = csv.writer(file) writer.writerow(to_append.split()) We’ll create a CSV file with the data we required. We’ll process dataset as per our requirements. Pip may not get latest tensorflow version so install tensorflow using this command: pip install -ignore-installed -upgrade Creating Dataset Install all the packages required and import them.Ĭheck the vers ion of tensorflow it should be greater than 1.1 otherwise various keras features will fail.

Import packages import librosa import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline import os import csv # Preprocessing from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, StandardScaler #Keras import keras

We’ll be using package Keras which uses tensorflow package at the backend. This dataset consists of 10 genres and each genre consist of 100 music clips each is 30 seconds long. If this site doesn’t work than you can get the dataset from here. We’ll use GTZAN genre collection dataset.

Microsoft audio processing music classification