We propose a framework for computer music composition that uses resilient propagation (RProp) and long short term memory (LSTM) recurrent neural network. In this paper, we show that LSTM network learns the structure and characteristics of music pieces properly by demonstrating its ability to recreate music. We also show that predicting existing music using RProp outperforms Back propagation through time (BPTT).
翻译:我们提出了一个计算机音乐构成框架,使用耐受性传播(RProp)和长期短期内存(LSTM)常规神经网络。 在本文中,我们展示了LSTM网络通过展示其重建音乐的能力来正确学习音乐片的结构和特点。 我们还展示了使用RProp预测现有音乐在时间上优于回传(BPTT ) 。