Deep Learning for MIR II: State-of-the-art Algorithms 2021
A survey of cutting-edge research in MIR using Deep Learning presented by instructors and a lineup of guest speakers leading research in industry and academia. Instructors will explain and demonstrate concepts in models that are used in cutting-edge industry and academic research. Students will tackle a real problem of their choice using deep learning models. Instructors will serve as advisors to students in the course on-demand.
Students will build and train state-of-the-art models using tensorflow and GPU computing, adapting them to a problem of their interest.
Theory includes: Generative models. Self-supervised feature learning. Attention mechanisms.
Models covered includes: DeepSpeech, Transformer, Crepe, GrFNNs
Practice: music and speech recognition/synthesis, beat-tracking, music-recommendation, and semantic analysis.
Prerequisites:
- Deep Learning for MIR I
- This course is meant for individuals who want to gain experience applying Deep Learning to solve a problem of their interest in MIR.
About the instructors:
Elena Georgieva Elena is a PhD student and researcher at NYU’s Music and Audio Research Lab (MARL). Before joining MARL, Elena taught sound recording and managed the recording studio at CCRMA, where she completed her masters degree in Music Science in Technology degree in 2019. Elena has expertise in music information retrieval, machine learning, sound recording, and vocals. Elena has presented her work at the ISMIR and ICML conferences, at Stanford and Berkeley, as well as at several tech companies. Elenatheodora.com
Iran R. Roman is a theoretical neuroscientist and machine listening scientist at New York University’s Music and Audio Research Laboratory. Iran is a passionate instructor, with extensive experience teaching artificial intelligence and deep learning. His industry experience includes deep learning engineering internships at Plantronics in 2017, Apple in 2018 and 2019, Oscilloscape in 2020, and Tesla in 2021. Iran’s research has focused on using deep learning for speech recognition and auditory scene analysis. iranroman.github.io
IMPORTANT: Contact the instructor before registering to confirm your eligibility. Attach a copy of your registration or diploma for the CCRMA Deep Learning for MIR I workshop.