Recommending Music with Waveform-based Architectures
In this talk we discuss deep models that use waveform representations as input to estimate an embedded space where distances become meaningful when recommending music. Such space is initially produced by analyzing listener behavior, thus becoming a powerful tool to recommend the most popular content in a given music catalog but weak in terms of addressing the so-called "cold start problem," i.e., to recommend content that has spun infrequently or never at all and thus, has little listener behavior data associated with it. We show how, given enough data, deep waveform-based architectures  can estimate such spaces more accurately than spectrogram-based ones. Moreover, by using other sources of data (e.g., human labeled music attributes such as the ones in the Music Genome Project) with late-fusion multimodal networks , we achieve higher accuracy when predicting these embedded spaces. Finally, several musical examples are explored to further illustrate the recommendation results.
 Pons, J., Nieto, O., Prockup, M., Schmidt, E., Ehmann, A., Serra, X., End-to-End Learning for Music Audio Tagging at Scale. Proc. of the 19th International Society for Music Information Retrieval Conference (ISMIR). Paris, France, 2018
 Oramas, S., Barbieri, F., Nieto, O., Serra, X., Multimodal Deep Learning for Music Genre Classification. Transactions of the International Society for Music Information Retrieval (TISMIR). 2018
Oriol Nieto is a Senior Scientist at Pandora, where he aims at improving the long tail music recommendations. Prior to that, he defended his Ph.D Dissertation in the Music and Audio Research Lab at NYU focusing on the automatic analysis of structure in music. He holds an M.A. in Music, Science, and Technology from the Center for Computer Research in Music and Acoustics at Stanford University, an M.S. in Information Theories from the Music Technology Group at Pompeu Fabra University, and a Bachelor’s degree in Computer Science from the Polytechnic University of Catalonia. His research focuses on music information retrieval, large scale recommendation systems, and machine learning with especial emphasis on deep architectures. Oriol plays guitar, violin, and sings (and screams) in his spare time.