  The version identification (VI) task deals with the automatic detection of
recordings that correspond to the same underlying musical piece. Despite many
efforts, VI is still an open problem, with much room for improvement, specially
with regard to combining accuracy and scalability. In this paper, we present
MOVE, a musically-motivated method for accurate and scalable version
identification. MOVE achieves state-of-the-art performance on two
publicly-available benchmark sets by learning scalable embeddings in an
Euclidean distance space, using a triplet loss and a hard triplet mining
strategy. It improves over previous work by employing an alternative input
representation, and introducing a novel technique for temporal content
summarization, a standardized latent space, and a data augmentation strategy
specifically designed for VI. In addition to the main results, we perform an
ablation study to highlight the importance of our design choices, and study the
relation between embedding dimensionality and model performance.
