![]() ![]() ![]() The paper pays special attention to new trends in machine learning applied to the music annotation problem. Its main goal is give the reader an overview of the history and the current state-of-the-art, exploring techniques and datasets used to the date, as well as identifying current challenges, such as this ambiguity of genre definitions or the introduction of human-centric approaches. In its first part, this paper offers a survey trying to cover the many different aspects of the matter. Music categorizations are vague and unclear, suffering from human subjectivity and lack of agreement. Although research has been prolific in terms of number of published works, the topic still suffers from a problem in its foundations: there is no clear and formal definition of what genre is. Music genre classification is one of the sub-disciplines of music information retrieval (MIR) with growing popularity among researchers, mainly due to the already open challenges. Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture, fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects. With an attempt to avoid discomfort to participants in performing long physical tasks for data recording, this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory (LSTM) neural networks. Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long. A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease. There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson's disease (PD).
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