Melody is a sequence of succession of tones and itself is the major part of a song composition. To accompany the melody, chord compositions will be prepared in accordance with the harmonization of tones within it. Composing chords is an unquantifiable process which may only be rated by subjective judgement. Variations are welcomed, however subjectively each person have their own preference thus making a generally likeable composition is a challenge to musicians since long. Simon et al. (2008) proposed a solution on composing chord accompaniment for a melody in real-time as an application named MySong. Machine learning serves a main part on the application, therefore it suggests that other machine learning variations may possibly applicable to the problem and possibly produce better result. Artificial neural network is considered as a potential alternative that may have an advantage on parameter customization and time-series applicable. Based on test results, it is proven that an art...

We describe a sequential neural network for harmonizing melodies in real-time. The networkmodels aspects of human cognition and can be used as the basis for building an interactive systemthat automatically generates accompaniment for simple melodies in live performance situations.The net learns relations between important notes of the melody and their harmonies and is ableto produce harmonies for new melodies in

We describe a sequential neural network for harmonizing melodies in real time. It models aspects of human cognition. This neural network succeeds reasonably well, if we take into consideration the constraints imposed by real time processing. The model exploits e ciently the available sequential information. The net contains a sub-net for meter that produces a periodic index of meter, providing the needed metric awareness. The net learns the relations between important notes of the melody and their harmonies and is able to produce harmonies for new melodies in real time, i.e., without knowledge of the future development of the melody.

In this work, an Elman recurrent neural network is used for automatic musical structure composition based on the style of a music previously learned during the training phase. Furthermore, a small fragment of a chaotic melody is added to the input layer of the neural network as an inspiration source to attain a greater variability of melodies. The neural network is trained by using the BPTT (back propagation through time) algorithm. Some melody measures are also presented for characterizing the melodies provided by the neural network and for analyzing the effect obtained by the insertion of chaotic inspiration in relation to the original melody characteristics. Specifically, a similarity melodic measure is considered for contrasting the variability obtained between the learned melody and each one of the composite melodies by using different quantities of inspiration musical notes.