Improved Music Feature Learning with Transfer Learning in Deep Neural Networks
Universiteit Twente , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Technische Natuurkunde - Department of Applied Physics, Elementaire Processen in Gasontladingen (EPG) , Vrije Universiteit Amsterdam, Faculteit der Bètawetenschappen (Faculty of Science), Afdeling Informatica (Computer Science), Artificial Intelligence , NWO-institutenorganisatie , Universiteit Twente, Faculty of Science and Technology (TNW), Chemical Engineering, Membrane Science and Technology (MST) , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Scheikundige Technologie - Department of Chemical Engineering and Chemistry, Membrane Materials and Processes , Universiteit Twente, Faculty of Science and Technology (TNW), Proceskunde , NWO-institutenorganisatie, DIFFER - Dutch Institute for Fundamental Energy Research , Technische Universiteit Eindhoven - Eindhoven University of Technology , Universiteit van Amsterdam , Universiteit van Amsterdam, Faculteit der Natuurwetenschappen, Wiskunde en Informatica (Faculty of Science), Instituut voor Informatica (IVI) , VU
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Universiteit Twente , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Technische Natuurkunde - Department of Applied Physics, Elementaire Processen in Gasontladingen (EPG) , Vrije Universiteit Amsterdam, Faculteit der Bètawetenschappen (Faculty of Science), Afdeling Informatica (Computer Science), Artificial Intelligence , NWO-institutenorganisatie , Universiteit Twente, Faculty of Science and Technology (TNW), Chemical Engineering, Membrane Science and Technology (MST) , Technische Universiteit Eindhoven - Eindhoven University of Technology, Faculteit Scheikundige Technologie - Department of Chemical Engineering and Chemistry, Membrane Materials and Processes , Universiteit Twente, Faculty of Science and Technology (TNW), Proceskunde , NWO-institutenorganisatie, DIFFER - Dutch Institute for Fundamental Energy Research , Technische Universiteit Eindhoven - Eindhoven University of Technology , Universiteit van Amsterdam , Universiteit van Amsterdam, Faculteit der Natuurwetenschappen, Wiskunde en Informatica (Faculty of Science), Instituut voor Informatica (IVI) , VU
As we look to advance the state of the art in content-based music informatics, there is a general sense that progress is decelerating throughout the field. On closer inspection, performance trajectories across several applications reveal that this is indeed the case: hand-crafted feature design is sub-optimal and unsustainable, the power of shallow architectures is fundamentally limited, and short-time analysis cannot encode musically meaningful structure. Acknowledging breakthroughs in other perceptual AI domains, we offer that deep learning holds the potential to overcome each of these obstacles. Consequentially, we believe that deep learning can advance the state-of-the-art in music genre recognition.
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