A Dataset and Baselines for Measuring and Predicting the Music Piece Memorability

TL;DR

This paper introduces a new dataset and baseline models for measuring and predicting music memorability, revealing that certain musical features influence how memorable a piece is.

Contribution

It presents the first data-driven deep learning approach to predict music memorability using a novel dataset and experimental methodology.

Findings

Higher valence, arousal, and faster tempo increase memorability

Predicting music memorability is feasible with limited data

Deep learning models can analyze interpretable features and spectrograms

Abstract

Nowadays, humans are constantly exposed to music, whether through voluntary streaming services or incidental encounters during commercial breaks. Despite the abundance of music, certain pieces remain more memorable and often gain greater popularity. Inspired by this phenomenon, we focus on measuring and predicting music memorability. To achieve this, we collect a new music piece dataset with reliable memorability labels using a novel interactive experimental procedure. We then train baselines to predict and analyze music memorability, leveraging both interpretable features and audio mel-spectrograms as inputs. To the best of our knowledge, we are the first to explore music memorability using data-driven deep learning-based methods. Through a series of experiments and ablation studies, we demonstrate that while there is room for improvement, predicting music memorability with limited data is possible. Certain intrinsic elements, such as higher valence, arousal, and faster tempo, contribute to memorable music. As prediction techniques continue to evolve, real-life applications like music recommendation systems and music style transfer will undoubtedly benefit from this new area of research.