Transfer learning by supervised pre-training for audio-based music classification

Very few large-scale music research datasets are publicly available. There is an increasing need for such datasets, because the shift from physical to digital distribution in the music industry has given the listener access to a large body of music, which needs to be cataloged efficiently and be easily browsable. Additionally, deep learning and feature learning techniques are becoming increasingly popular for music information retrieval applications, and they typically require large amounts of training data to work well. In this paper, we propose to exploit an available large-scale music dataset, the Million Song Dataset (MSD), for classification tasks on other datasets, by reusing models trained on the MSD for feature extraction. This transfer learning approach, which we refer to as supervised pre-training, was previously shown to be very effective for computer vision problems. We show that features learned from MSD audio fragments in a supervised manner, using tag labels and user listening data, consistently outperform features learned in an unsupervised manner in this setting, provided that the learned feature extractor is of limited complexity. We evaluate our approach on the GTZAN, 1517-Artists, Unique and Magnatagatune datasets

Sample-level CNN Architectures for Music Auto-tagging Using Raw Waveforms

Recent work has shown that the end-to-end approach using convolutional neural network (CNN) is effective in various types of machine learning tasks. For audio signals, the approach takes raw waveforms as input using an 1-D convolution layer. In this paper, we improve the 1-D CNN architecture for music auto-tagging by adopting building blocks from state-of-the-art image classification models, ResNets and SENets, and adding multi-level feature aggregation to it. We compare different combinations of the modules in building CNN architectures. The results show that they achieve significant improvements over previous state-of-the-art models on the MagnaTagATune dataset and comparable results on Million Song Dataset. Furthermore, we analyze and visualize our model to show how the 1-D CNN operates.Comment: Accepted for publication at ICASSP 201