The modeling of artificial, human-level creativity is becoming more and more achievable. In
recent years, neural networks have been successfully applied to different tasks such as …

With the introduction of ChatGPT, the public's perception of AI-generated content has begun
to reshape. Artificial intelligence has significantly reduced the barrier to entry for non …

Music creation is difficult because one must express one's creativity while following strict
rules. The advancement of deep learning technologies has diversified the methods to …

In the field of content generation by machine, the state-of-the-art text-to-image model,
DALL⋅ E, has advanced and diverse capacities for the combinational image generation with …

In this paper, we present melody2vec, an extension of the word2vec framework to melodies.
To apply the word2vec framework to a melody, a definition of a word within a melody is …

Musical co-creativity aims at making humans and computers collaborate to compose music.
As an MIR team in computational musicology, we experimented with co-creativity when …

The purpose of this research is to present a natural language processing-based approach to
symbolic music analysis. We propose Mel2Word, a text-based representation including pitch …

This paper proposes a new approach to represent music using a fixed time-step with a view
to improve the quality of the music generated through recurrent neural networks. The …

This paper presents MahlerNet, a deep recurrent neural network that models polyphonic
music sequences of arbitrary length with an arbitrary number of instruments. The data …

Efficient synthesis of musical sequences is a challenging task from a machine learning
perspective, as human perception is aware of the global context to shorter sequences as …