  We present a supervised neural network model for polyphonic piano music
transcription. The architecture of the proposed model is analogous to speech
recognition systems and comprises an acoustic model and a music language model.
The acoustic model is a neural network used for estimating the probabilities of
pitches in a frame of audio. The language model is a recurrent neural network
that models the correlations between pitch combinations over time. The proposed
model is general and can be used to transcribe polyphonic music without
imposing any constraints on the polyphony. The acoustic and language model
predictions are combined using a probabilistic graphical model. Inference over
the output variables is performed using the beam search algorithm. We perform
two sets of experiments. We investigate various neural network architectures
for the acoustic models and also investigate the effect of combining acoustic
and music language model predictions using the proposed architecture. We
compare performance of the neural network based acoustic models with two
popular unsupervised acoustic models. Results show that convolutional neural
network acoustic models yields the best performance across all evaluation
metrics. We also observe improved performance with the application of the music
language models. Finally, we present an efficient variant of beam search that
improves performance and reduces run-times by an order of magnitude, making the
model suitable for real-time applications.
