Author/Authors :
Bryan Pardo and Jonah Shifrin، نويسنده , , William Birmingham، نويسنده ,
Abstract :
We have created a system for music search and retrieval.
A user sings a theme from the desired piece of
music. The sung theme (query) is converted into a sequence
of pitch-intervals and rhythms. This sequence is
compared to musical themes (targets) stored in a database.
The top pieces are returned to the user in order of
similarity to the sung theme. We describe, in detail, two
different approaches to measuring similarity between
database themes and the sung query. In the first, queries
are compared to database themes using standard
string-alignment algorithms. Here, similarity between
target and query is determined by edit cost. In the second
approach, pieces in the database are represented
as hidden Markov models (HMMs). In this approach, the
query is treated as an observation sequence and a target
is judged similar to the query if its HMM has a high
likelihood of generating the query. In this article we
report our approach to the construction of a target database
of themes, encoding, and transcription of user
queries, and the results of preliminary experimentation
with a set of sung queries. Our experiments show that
while no approach is clearly superior to the other system,
string matching has a slight advantage. Moreover,
neither approach surpasses human performance
