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Automatic Music Transcription by Deep Learning Dan Teodor Avramescu, Paul Stefan Popescu, Mihai Mocanu, Marian Cristian Mihăescu |
87-100 |
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Opportunities for the development of students’ skills and creativity during the pandemic Gabriel Gorghiu, Elena Ancuța Santi, Costin Pribeanu |
101-117 |
University of Craiova, Department of Computers and Information Technology
Blvd. Decebal nr. 107, RO-200440, Craiova, Romania
Abstract. Automatic music transcription represents a specific translation task that falls into information processing, where input information is sound. Similar, but more general, are building text transcripts from speech. We propose a data analysis pipeline that follows the roadmap of previous works but uses a distinct dataset and several custom hyperparameter settings. The results are not as good as previous ones as the implementation runs on commodity systems, and therefore refinements and more powerful techniques may further improve the results. Still, state-of-the-art DL libraries raise the potential for improvements in a susceptible application domain.
Keywords: automatic music transcription; deep learning.
Cite this paper as:
Avramescu, D. T., Popescu, P. S., Mocanu, M., Mihăescu, M. C. Automatic Music Transcription by Deep Learning.
International Journal of User-System Interaction 14(3),
87-100, 2021.
1 Valahia University Targoviste
13 Aleea Sinaia, 130004, Targoviste, Romania
2 Academy of Romanian Scientists
Splaiul Indpendentei 54, Bucharest, Romania
Abstract. The coronavirus pandemic had a major impact on higher education by forcing the universities to go online. Despite many disadvantages in terms of socialization, face-to-face interaction, attention, and mental health, online teaching and learning provide many opportunities in terms of time management and personal development. This research is exploring the opportunities for the development of students’ skills and creativity during the pandemic. A research model has been developed that analyzes the relationships between the quality of teaching, the quality of online activities, the stimulation of communication with the teacher and other students, and the opportunities for personal development. The model testing results show that the quality of online activities, measured in terms of stimulation, motivation, and attractivity, is the main predictor of the opportunities for skills and creativity development.
Keywords: quality of the online school, pandemic, personal development.
Cite this paper as:
Gorghiu, G., Santi, E. A., Pribeanu, C. Opportunities for the development of students’ skills and creativity during the pandemic.
International Journal of User-System Interaction 14(3),
101-117, 2021.