Music Era Recognition Using Supervised Contrastive Learning and Artist Information

TL;DR

This paper introduces a novel approach for music era recognition using supervised contrastive learning, leveraging audio features and artist information to improve classification accuracy for playlist and recommendation systems.

Contribution

It proposes a new multimodal contrastive learning framework that combines audio and artist data to enhance music era classification accuracy.

Findings

Audio-based model achieves 54% accuracy within 3-year tolerance.

Incorporating artist information improves accuracy by 9%.

Multimodal contrastive learning enhances music era recognition performance.

Abstract

Does popular music from the 60s sound different than that of the 90s? Prior study has shown that there would exist some variations of patterns and regularities related to instrumentation changes and growing loudness across multi-decadal trends. This indicates that perceiving the era of a song from musical features such as audio and artist information is possible. Music era information can be an important feature for playlist generation and recommendation. However, the release year of a song can be inaccessible in many circumstances. This paper addresses a novel task of music era recognition. We formulate the task as a music classification problem and propose solutions based on supervised contrastive learning. An audio-based model is developed to predict the era from audio. For the case where the artist information is available, we extend the audio-based model to take multimodal inputs and develop a framework, called MultiModal Contrastive (MMC) learning, to enhance the training. Experimental result on Million Song Dataset demonstrates that the audio-based model achieves 54% in accuracy with a tolerance of 3-years range; incorporating the artist information with the MMC framework for training leads to 9% improvement further.