  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.
