GENPIA: A Genre-Conditioned Piano Music Generation System

With the demand for music continuing to grow as people seek variety and personal resonance, many works focus on music generation. In this study, we propose GENPIA, a genre-conditioned piano music generation system. The system encompasses Anime, R&B, Jazz, and Classical music genres. To build our system, we collect and label audio data of various genres for the specific objective of our research. REMI audio representation with genre information extension is applied during data pre-processing to present the audio data with a better data structure. Transformer-XL is implemented as the model to learn knowledge about the extended audio representation and generate the desired output audio. An external dataset, called Ailabs.Tw lK7, is utilized for pre-Training purposes. The results obtained from a listening questionnaire show that GENPIA can generate better piano pieces conditioned on different genres compared to the prior state-of-The-Art work.