Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model

TL;DR

This paper introduces Musical Attention, a novel attention mechanism for Transformers that incorporates musical metadata to improve the coherence, diversity, and naturalness of generated music.

Contribution

The study proposes a music-specific attention model that explicitly leverages musical structure and metadata, enhancing Transformer-based music generation quality.

Findings

Outperforms prior methods like Full and Strided Attention in coherence and diversity.

Reduces note repetition and improves harmonic consistency.

Generates more natural and expressive musical compositions.

Abstract

This study aims to enhance the quality of music generation using Transformers by incorporating meta-information. While Transformer-based approaches are effective at capturing long-term dependencies in musical compositions, the music they generate often suffers from issues such as excessive repetition or duplication of notes, leading to unnatural melodies. To address these limitations, we propose Musical Attention, a mechanism that incorporates meta-information such as bar numbers, key, signatures, and tempos into the attention process. Musical Attention explicitly leverages both the structural properties of music and its associated metadata, enabling the Transformer's attention mechanism to operate more effectively and thereby improving the quality of the generated output. In our framework, each musical note is represented as a combination of five events-pitch, bar number, onset, duration, and velocity in addition to the three metadata elements. The attention mechanism is then modified to reflect the correlations among these eight features, allowing the model to better capture the inherent characteristics of musical composition. Experimental results demonstrate that the model incorporating Musical Attention outperforms prior methods, such as Full Attention and Strided Attention, in terms of musical coherence, variation, and overall quality. Notably, it significantly reduces repetition and enhances the model's ability to generate diverse, harmonically consistent melodies. Musical Attention thus represents a meaningful advancement in AI-driven music generation, facilitating the creation of more natural and expressive compositions.