# Performer Identification From Symbolic Representation of Music Using Statistical Models

arXiv: https://arxiv.org/abs/2108.02576 · HTML: https://mlsanity.com/paper/2108.02576/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 30 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Transfer Learning for Piano Sustain-Pedal Detection](https://mlsanity.com/paper/2103.13219/) (2021)

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

- [Reconstructing Human Expressiveness in Piano Performances with a Transformer Network](https://mlsanity.com/paper/2306.06040/) (2023)

## Abstract (arXiv)

Music Performers have their own idiosyncratic way of interpreting a musical piece. A group of skilled performers playing the same piece of music would likely to inject their unique artistic styles in their performances. The variations of the tempo, timing, dynamics, articulation etc. from the actual notated music are what make the performers unique in their performances. This study presents a dataset consisting of four movements of Schubert's ``Sonata in B-flat major, D.960" performed by nine virtuoso pianists individually. We proposed and extracted a set of expressive features that are able to capture the characteristics of an individual performer's style. We then present a performer identification method based on the similarity of feature distribution, given a set of piano performances. The identification is done considering each feature individually as well as a fusion of the features. Results show that the proposed method achieved a precision of 0.903 using fusion features. Moreover, the onset time deviation feature shows promising result when considered individually.