We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections.
Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks.
Recently, the end-to-end approach that learns hierarchical representations from raw data using deep convolutional neural networks has been successfully explored in the image, text and speech domains.
Recently deep learning based recommendation systems have been actively explored to solve the cold-start problem using a hybrid approach.
Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip.
Feature learning and deep learning have drawn great attention in recent years as a way of transforming input data into more effective representations using learning algorithms.