# Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion

## TL;DR

This paper introduces a new method using similarity network fusion to automatically identify hierarchical musical structures from audio, improving agreement with human annotations by combining multiple features without specialized heuristics.

## Contribution

The novel pipeline employs SNF to fuse diverse features for better music structure segmentation, supporting multiple feature types simultaneously without algorithm modifications.

## Key findings

- SNF improves alignment with human annotations
- Supports multiple feature types simultaneously
- Outperforms prior spectral clustering methods

## Abstract

We describe a novel pipeline to automatically discover hierarchies of repeated sections in musical audio. The proposed method uses similarity network fusion (SNF) to combine different frame-level features into clean affinity matrices, which are then used as input to spectral clustering. While prior spectral clustering approaches to music structure analysis have pre-processed affinity matrices with heuristics specifically designed for this task, we show that the SNF approach directly yields segmentations which agree better with human annotators, as measured by the ``L-measure'' metric for hierarchical annotations. Furthermore, the SNF approach immediately supports arbitrarily many input features, allowing us to simultaneously discover structure encoded in timbral, harmonic, and rhythmic representations without any changes to the base algorithm.