Automated composition of Galician Xota — tuning RNN-based composers for specific musical styles using deep Q-learning

Music composition is a complex field that is difficult to automate because the computational definition of what is good or aesthetically pleasing is vague and subjective. Many neural network-based methods have been applied in the past, but they lack consistency and in most cases, their outputs fail to impress. The most common issues include excessive repetition and a lack of style and structure, which are hallmarks of artificial compositions. In this project, we build on a model created by Magenta—the RL Tuner—extending it to emulate a specific musical genre—the Galician Xota. To do this, we design a new rule-set containing rules that the composition should follow to adhere to this style. We then implement them using reward functions, which are used to train the Deep Q Network that will be used to generate the pieces. After extensive experimentation, we achieve an implementation of our rule-set that effectively enforces each rule on the generated compositions, and outline a solid research methodology for future researchers looking to use this architecture. Finally, we propose some promising future work regarding further applications for this model and improvements to the experimental procedure

Process Mining of Automation Services with Long Short-Term Memory Neural Networks

Deep learning provides a wide scope of valuable tools for operating with and analysing various kinds of data sources. To optimise the workload of employees, Posti Group Oyj, the foremost organisation in the sphere of postal and logistics maintenance in Finland, was applied as a case study. The primary target of the research was to elaborate a supervised machine learning model for the classification of eight-hour work shifts of mail carriers and the prediction of verification states for the inbound work shifts. Convolutional long short-term memory neural network was deployed as a baseline model since each work shift represents a sequence of postal tasks. The CNN LSTM network with the best-observed performance was deduced through carrying out the chain of the experiments, devoted to the network depth analysis and hyperparameter tuning procedure. The performance of the implemented models was assessed regarding a broad spectrum of the evaluation metrics, including classification accuracy, precision and recall measures, F-score, the area under the receiver operating characteristic curve score, and binary cross-entropy loss, utilising novel hyperparameter optimisation tool, called Talos. Furthermore, developed convolutional LSTM network substantially outperformed traditional machine learning approaches such as support vector machine, logistic regression, random decision forest, gradient boosting decision tree, k-nearest neighbours, and adaptive boosting classifiers. The devised CNN LSTM network will be integrated into a software as a service application for the prediction of the verification statuses of novel work shifts. Moreover, supplementary analyses were conducted to determine incoming postal task and its timestamp within a work shift and predict potential postal jobs suffixes. Like in the previous research, LSTM network was maintained as a baseline algorithm. In both studies, the elaborated models obtained essentially better performance, compared to state-of-the-art algorithms