music-classifier | Classifying music by genres via deep learning

music-classifier is a Python library. music-classifier has no bugs, it has no vulnerabilities and it has low support. However music-classifier build file is not available. You can download it from GitHub.

Your task is to classify music files into 10 predetermined genres such as jazz, classical, country, pop, rock, and metal. We will use the GTZAN dataset, which is frequently used to benchmark music genre classification tasks. It is organized into 10 distinct genres: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, and rock. The dataset contains the first 30 seconds of 100 songs per genre. You can download the dataset from UNM Learn. In the folder genres you will find 10 folders, one per genre and 90 songs per folder. Additionally, you’ll find a validation folder with 100 unlabeled songs. The tracks are recorded at 22,050 Hz (22,050 readings per second) mono in the au format. Design a learning experiment capable of predicting music genres given their audio. Here is the link of the competition on Kaggle.