Generating melodic dictations using Markov Chains and LSTM neural networks

Stefanowska E, Kacprzak S, Ksi??ek P. Generating melodic dictations using Markov Chains and LSTM neural networks. In: AES Convention 154. Audio Engineering Society; 2023. Paper 10647. Available from: https://aes.org/publications/elibrary-page/?id=22054

@inproceedings{Stefanowska2023_22054,
  author = {Stefanowska, Emilia and Kacprzak, Stanis?aw and Ksi??ek, Piotr},
  title = {{Generating melodic dictations using Markov Chains and LSTM neural networks}},
  booktitle = {AES Convention 154},
  note = {Paper 10647},
  year = {2023},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22054}
}

TY  - CPAPER
TI  - Generating melodic dictations using Markov Chains and LSTM neural networks
AU  - Stefanowska, Emilia
AU  - Kacprzak, Stanis?aw
AU  - Ksi??ek, Piotr
T2  - AES Convention 154
M1  - Paper 10647
PY  - 2023
DA  - 2023/05/06
UR  - https://aes.org/publications/elibrary-page/?id=22054
PB  - Audio Engineering Society
LA  - en
AB  - Melodic dictations are aural training exercises that require students to transcribe the melody they hear into musical notation. In this paper, we propose three algorithms that generate single-voice melodies that could be serve as melodic dictations. The first algorithm utilizes a higher-order Markov Chain model to generate melodic patterns based on a given data set of training set dictations. The second algorithm employs a neural network with Long Short-Term Memory (LSTM) layers and the Bahdanau attention mechanism. The third algorithm generates melodies by choosing each note randomly. We analyzed the generated dictations using the dissimilarity index based on the cross-correlation, to demonstrate that the algorithms generate novel and diverse melodic dictations. To evaluate the musical quality of the melodies, we conducted a survey in which professional music theory teachers graded the dictations from the training set and those generated by the algorithms. The results indicate that some of the generated dictations are comparable in quality to those in the training set and could find potential applications in musical education.
ER  -