  We investigate the problem of incorporating higher-level symbolic score-like
information into Automatic Music Transcription (AMT) systems to improve their
performance. We use recurrent neural networks (RNNs) and their variants as
music language models (MLMs) and present a generative architecture for
combining these models with predictions from a frame level acoustic classifier.
We also compare different neural network architectures for acoustic modeling.
The proposed model computes a distribution over possible output sequences given
the acoustic input signal and we present an algorithm for performing a global
search for good candidate transcriptions. The performance of the proposed model
is evaluated on piano music from the MAPS dataset and we observe that the
proposed model consistently outperforms existing transcription methods.
