首页|Recent Findings from Polytechnic University Bari Provides New Insights into Mach ine Learning (Neuralpmg: a Neural Polyphonic Music Generation System Based On Ma chine Learning Algorithms)
Recent Findings from Polytechnic University Bari Provides New Insights into Mach ine Learning (Neuralpmg: a Neural Polyphonic Music Generation System Based On Ma chine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in Machine Learning. According to news reporting originating from Bari, Italy, by NewsRx corresponden ts, research stated, “The realm of music composition, augmented by technological advancements such as computers and related equipment, has undergone significant evolution since the 1970s. In the field algorithmic composition, however, the i ncorporation of artificial intelligence (AI) in sound generation and combination has been limited.” Funders for this research include Politecnico di Bari, SECURE SAFE APULIA, CTEMT - “Casa delle Tecnologie Emergenti di Matera. Our news editors obtained a quote from the research from Polytechnic University Bari, “Existing approaches predominantly emphasize sound synthesis techniques, w ith no music composition systems currently employing Nicolas Slonimsky’s theoret ical framework. This article introduce NeuralPMG, a computer-assisted polyphonic music generation framework based on a Leap Motion (LM) device, machine learning (ML) algorithms, and brain-computer interface (BCI). ML algorithms are employed to classify user’s mental states into two categories: focused and relaxed. Inte raction with the LM device allows users to define a melodic pattern, which is el aborated in conjunction with the user’s mental state as detected by the BCI to g enerate polyphonic music. NeuralPMG was evaluated through a user study that invo lved 19 students of Electronic Music Laboratory at a music conservatory, all of whom are active in the music composition field. The study encompassed a comprehe nsive analysis of participant interaction with NeuralPMG. The compositions they created during the study were also evaluated by two domain experts who addressed their aesthetics, innovativeness, elaboration level, practical applicability, a nd emotional impact.”
BariItalyEuropeAlgorithmsCyborgsEmerging TechnologiesMachine LearningPolytechnic University Bari
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