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We present a neural network model that learns to produce music scores directly from audio signals. Instead of employing commonplace processing steps, such as frequency transform front-ends, harmonicity and scale priors, or temporal pitch smoothing, we show that a neural network can learn such steps on its own when presented with the appropriate training data. We show how such a network can perform...
We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation. Due to the extensibility of this approach we show how we can achieve better source separation performance as compared to NMF-based methods, and propose a variety of derivative architectures that can be used for further...
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