Polyphonic Music Modelling with LSTM-RTRBM

Qi Lyu, Zhiyong Wu, Jun Zhu
2015 Proceedings of the 23rd ACM international conference on Multimedia - MM '15  
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
more » ... ic 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.
doi:10.1145/2733373.2806383 dblp:conf/mm/LyuWZ15 fatcat:yop3t3tcw5apxbhk6gmn4puo5i