  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.
