Polyphonic Music Modelling with LSTM-RTRBM

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Abstract
Recent interest in music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of the recent advancements in neural networks. The challenges of developing effective browsing, searching and organization techniques for the growing bodies of music collections call for more powerful statistical models. In this paper, we present LSTM-RTRBM, a new neural network model for the problem of creating accurate yet flexible models of polyphonic music. Our model integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our approach greatly improves the performance of polyphonic music sequence modelling, achieving the state-of-the-art results on multiple datasets.
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Funding Information
  • National Natural Science Foundation of China (61375027, 61433018, 61322308 and 61332007)
  • National High Technology Research and Development Program of China (2015AA016305)
  • Tsinghua Initiative Scientific Research Program (20121088071)
  • Major Program for National Social Science Foundation of China 13 (ZD189)