Piece Identification in Classical Piano Music Without Reference Scores

TL;DR

This paper presents a robust system for identifying classical piano pieces from audio excerpts without reference scores, using automatically compiled internet-based performance databases and fingerprinting, overcoming transcription noise and performance variability.

Contribution

It introduces a method to automatically build a reference database from internet performances and improves identification accuracy through redundancy and performance rating strategies.

Findings

High accuracy identification achieved without manual data annotation.

Enhanced robustness by increasing reference database redundancy.

Effective preprocessing to select suitable reference performances.

Abstract

In this paper we describe an approach to identify the name of a piece of piano music, based on a short audio excerpt of a performance. Given only a description of the pieces in text format (i.e. no score information is provided), a reference database is automatically compiled by acquiring a number of audio representations (performances of the pieces) from internet sources. These are transcribed, preprocessed, and used to build a reference database via a robust symbolic fingerprinting algorithm, which in turn is used to identify new, incoming queries. The main challenge is the amount of noise that is introduced into the identification process by the music transcription algorithm and the automatic (but possibly suboptimal) choice of performances to represent a piece in the reference database. In a number of experiments we show how to improve the identification performance by increasing redundancy in the reference database and by using a preprocessing step to rate the reference performances regarding their suitability as a representation of the pieces in question. As the results show this approach leads to a robust system that is able to identify piano music with high accuracy -- without any need for data annotation or manual data preparation.