2017/03/06 by Jongpil Lee, Ji Young Park, Lee, Jongpil +5 · 3 citations
Arts and Humanities · Computer Science · #Diverse Musicological Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimedia (cs.MM) #Music and Audio Processing #Neural and Evolutionary Computing (cs.NE) #Sound (cs.SD) #Speech Recognition and Synthesis
paper · pdf · doi:10.48550/arxiv.1703.01789
openalex publication_date 2017/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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. This approach was applied to musical signals as well but has been not fully explored yet. To this end, we propose sample-level deep convolutional neural networks which learn representations from very small grains of waveforms (e.g. 2 or 3 samples) beyond typical frame-level input representations. Our experiments show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million Song Dataset. In addition, we visualize filters learned in a sample-level DCNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency along layer, such as mel-frequency spectrogram that is widely used in music classification systems.