Long Short-Term Memory (LSTM) neural networks have been effectively applied on learning
and generating musical sequences, powered by sophisticated musical representations and …

Considering music as a sequence of events with multiple complex dependencies, the Long
Short-Term Memory (LSTM) architecture has proven very efficient in learning and …

Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for
artificial intelligence research. Despite recent successes of long-short term memory (LSTM) …

The recent explosion of interest in deep neural networks has affected and in some cases
reinvigorated work in fields as diverse as natural language processing, image recognition …

Over the past several years, deep learning for sequence modeling has grown in popularity.
To achieve this goal, LSTM network structures have proven to be very useful for making …

Use of deep learning techniques for generating music has gained popularity in the recent
years owing to the high amount of compute power being available and the evolution of deep …

A model of music needs to have the ability to recall past details and have a clear, coherent
understanding of musical structure. Detailed in the paper is a neural network architecture …

Traditionally, music was treated as an analogue signal and was generated manually. In
recent years, music is conspicuous to technology which can generate a suite of music …

Advances in machine learning have the potential to radically reshape interactions between
humans and computers. Deep learning makes it possible to discover powerful …

Abstract Machine learning has shown a successful component of methods for automatic
music composition. Considering music as a sequence of events with multiple complex …