Effective retrieval of polyphonic audio with polyphonic symbolic queries

Accurately finding audio recordings in response to symbolic queries is one of the key challenges in the field of music information retrieval. Pitch is one of the main features of music; in this paper we propose and evaluate approaches for using pitch information in polyphonic symbolic queries to retrieve full tracks of audio recordings. The audio data is first converted into symbolic data, using an automated transcription process. This is a noisy process, adding up to three times as many notes to the transcription than are actually present. Nevertheless, recordings can be accurately retrieved by manually-constructed queries (either in full or truncated) using the longest common subsequence algorithm (and a sliding window if the queries are truncated). Precision at 1 of about 80% was achieved, and around 85% of queries return correct answers in the top 10 from a collection of 1808 recordings. Truncated queries are as effective as untruncated queries for retrieving correct answers in the first rank position. Thus, the burden on users is reduced as they only need to produce a small fraction of a song as a query.