Memorization of melodies by complex-valued recurrent network

Memorization of the temporal sequences is very important and interesting problem. One of the examples is memorization of melodies. When we memorize melodies, we can recall it from a part. The function can be considered as an associative memory of the temporal sequences. From a technical point of view, it can be considered that the rest of the melody can be determined by some leading some notes. In this paper, we apply the complex-valued recurrent network, which has much superior ability to deal with temporal sequences than the conventional real-valued networks, to memorization of melodies. It is shown in computer simulations that the network can memorize some melodies and recall them correctly from any part.

[1]  Ronald J. Williams,et al.  Experimental Analysis of the Real-time Recurrent Learning Algorithm , 1989 .

[2]  Giovanni Soda,et al.  Local Feedback Multilayered Networks , 1992, Neural Computation.

[3]  Yuzo Hirai A model of human associative processor (HASP) , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[5]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[6]  Tadao Nakamura,et al.  Backpropagation networks including time delay elements (BPD) , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[7]  Yuzo Hirai,et al.  Psychological validity of a model of human associative processor (hasp) , 1988, Systems and Computers in Japan.

[8]  Michael R. Davenport,et al.  Continuous-time temporal back-propagation with adaptable time delays , 1993, IEEE Trans. Neural Networks.

[9]  Alexander H. Waibel,et al.  The Tempo 2 Algorithm: Adjusting Time-Delays By Supervised Learning , 1990, NIPS.

[10]  Michael I. Jordan Serial Order: A Parallel Distributed Processing Approach , 1997 .

[11]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[12]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[13]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[14]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[15]  Amir F. Atiya,et al.  Application of the recurrent multilayer perceptron in modeling complex process dynamics , 1994, IEEE Trans. Neural Networks.

[16]  Katsunori Shimohara,et al.  Subconnection neural network for event-driven temporal sequence processing , 1993, Neural Networks.

[17]  Kaoru Nakano,et al.  Associatron-A Model of Associative Memory , 1972, IEEE Trans. Syst. Man Cybern..

[18]  James L. McClelland,et al.  Finite State Automata and Simple Recurrent Networks , 1989, Neural Computation.

[19]  Masafumi Hagiwara,et al.  Learning Temporal Sequences by Complex Neurons with Local Feedback , 1996 .

[20]  Masafumi Hagiwara Multidirectional associative memory , 1990 .