Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity

TL;DR

This paper introduces a multi-input neural network that processes various audio features to model and analyze multi-dimensional music similarity, outperforming existing methods and providing insights into how different musical factors influence similarity perception.

Contribution

The paper presents a novel multi-input architecture that directly models human multi-dimensional music similarity using disentangled features from multiple audio representations.

Findings

Outperforms state-of-the-art similarity prediction methods

Effectively models multiple musical dimensions such as genre, mood, and tempo

Provides a multi-dimensional analysis of factors influencing music similarity

Abstract

In the context of music information retrieval, similarity-based approaches are useful for a variety of tasks that benefit from a query-by-example scenario. Music however, naturally decomposes into a set of semantically meaningful factors of variation. Current representation learning strategies pursue the disentanglement of such factors from deep representations, resulting in highly interpretable models. This allows the modeling of music similarity perception, which is highly subjective and multi-dimensional. While the focus of prior work is on metadata driven notions of similarity, we suggest to directly model the human notion of multi-dimensional music similarity. To achieve this, we propose a multi-input deep neural network architecture, which simultaneously processes mel-spectrogram, CENS-chromagram and tempogram in order to extract informative features for the different disentangled musical dimensions: genre, mood, instrument, era, tempo, and key. We evaluated the proposed music similarity approach using a triplet prediction task and found that the proposed multi-input architecture outperforms a state of the art method. Furthermore, we present a novel multi-dimensional analysis in order to evaluate the influence of each disentangled dimension on the perception of music similarity.