Augmenting Text-Based Music Retrieval with Audio Similarity.
Sprache des Titels:
Proceedings of the 10th International Conference on Music Information Retrieval (ISMIR 2009)
We investigate an approach to a music search engine
that indexesmusic pieces based on relatedWeb documents.
This allows for searching for relevant music pieces by issuing
descriptive textual queries. In this paper, we examine
the effects of incorporating audio-based similarity into
the text-based ranking process – either by directly modifying
the retrieval process or by performing post-hoc audiobased
re-ranking of the search results. The aimof this combination
is to improve ranking quality by including relevant
tracks that are left out by text-based retrieval approaches.
Our evaluations show overall improvements but also expose
limitations of these unsupervised approaches to combining
sources. Evaluations are carried out on two collections,
one large real-world collection containing about
35,000 tracks and on the CAL500 set.