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We investigate the problem of incorporating higher-level symbolic score-likeinformation into Automatic Music Transcription (AMT) systems to improve theirperformance. We use recurrent neural networks (RNNs) and their variants asmusic language models (MLMs) and present a generative architecture forcombining 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 giventhe acoustic input signal and we present an algorithm for performing a globalsearch for good candidate transcriptions. The performance of the proposed modelis evaluated on piano music from the MAPS dataset and we observe that theproposed model consistently outperforms existing transcription methods.

A Hybrid Recurrent Neural Network For Music Transcription