Attentional networks for music generation

Autor:	Keerti, Gullapalli, Vaishnavi, A N, Mukherjee, Prerana, Vidya, A Sree, Sreenithya, Gattineni Sai, Nayab, Deeksha
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Realistic music generation has always remained as a challenging problem as it may lack structure or rationality. In this work, we propose a deep learning based music generation method in order to produce old style music particularly JAZZ with rehashed melodic structures utilizing a Bi-directional Long Short Term Memory (Bi-LSTM) Neural Network with Attention. Owing to the success in modelling long-term temporal dependencies in sequential data and its success in case of videos, Bi-LSTMs with attention serve as the natural choice and early utilization in music generation. We validate in our experiments that Bi-LSTMs with attention are able to preserve the richness and technical nuances of the music performed.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2002.03854 Zobrazit plný text záznamu View this record from Arxiv