With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord m...With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.展开更多
根据现有复制策略在局部节点故障时数据查找失败率高的缺点,提出一种针对Chord网络的数据复制方法——Rd-Chord(rearranged replication method based on Chord)。利用离散存储的方法,将数据复制到Chord覆盖网根节点前继相对分散的节点...根据现有复制策略在局部节点故障时数据查找失败率高的缺点,提出一种针对Chord网络的数据复制方法——Rd-Chord(rearranged replication method based on Chord)。利用离散存储的方法,将数据复制到Chord覆盖网根节点前继相对分散的节点中,即使某个甚至几个区域节点全部故障,其他区域依然有数据副本可供使用。同时,为了维护网络结构和key迁移,针对Rd-Chord提出基础更新和定期更新2种更新策略。为了验证该方法的优越性,通过计算机仿真对前继复制、后继复制和Rd-Chord方法进行了大量的比较实验。实验结果表明,Rd-Chord方法能够解决节点区域性故障问题,在保证平均查找效率的前提下,查找失败率降低了近10%,明显优于其他方法。展开更多
基金National Natural Science Foundation of China (No.61801106)。
文摘With the continuous development of deep learning and artificial neural networks(ANNs), algorithmic composition has gradually become a hot research field. In order to solve the music-style problem in generating chord music, a multi-style chord music generation(MSCMG) network is proposed based on the previous ANN for creation. A music-style extraction module and a style extractor are added by the network on the original basis;the music-style extraction module divides the entire music content into two parts, namely the music-style information Mstyleand the music content information Mcontent. The style extractor removes the music-style information entangled in the music content information. The similarity of music generated by different models is compared in this paper. It is also evaluated whether the model can learn music composition rules from the database. Through experiments, it is found that the model proposed in this paper can generate music works in the expected style. Compared with the long short term memory(LSTM) network, the MSCMG network has a certain improvement in the performance of music styles.
文摘根据现有复制策略在局部节点故障时数据查找失败率高的缺点,提出一种针对Chord网络的数据复制方法——Rd-Chord(rearranged replication method based on Chord)。利用离散存储的方法,将数据复制到Chord覆盖网根节点前继相对分散的节点中,即使某个甚至几个区域节点全部故障,其他区域依然有数据副本可供使用。同时,为了维护网络结构和key迁移,针对Rd-Chord提出基础更新和定期更新2种更新策略。为了验证该方法的优越性,通过计算机仿真对前继复制、后继复制和Rd-Chord方法进行了大量的比较实验。实验结果表明,Rd-Chord方法能够解决节点区域性故障问题,在保证平均查找效率的前提下,查找失败率降低了近10%,明显优于其他方法。
