# Cover Song Identification with Timbral Shape Sequences

arXiv: https://arxiv.org/abs/1507.05143 · HTML: https://mlsanity.com/paper/1507.05143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2015.

## Built on by (most cited first, 1 of 1)

- [A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-By-Humming Task](https://mlsanity.com/paper/2312.01092/) (2023)

## Abstract (arXiv)

We introduce a novel low level feature for identifying cover songs which quantifies the relative changes in the smoothed frequency spectrum of a song. Our key insight is that a sliding window representation of a chunk of audio can be viewed as a time-ordered point cloud in high dimensions. For corresponding chunks of audio between different versions of the same song, these point clouds are approximately rotated, translated, and scaled copies of each other. If we treat MFCC embeddings as point clouds and cast the problem as a relative shape sequence, we are able to correctly identify 42/80 cover songs in the "Covers 80" dataset. By contrast, all other work to date on cover songs exclusively relies on matching note sequences from Chroma derived features.