  A naive approach for finding similar audio items would be to compare each
entry from the feature vector of the test example with each feature vector of
the candidates in a k-nearest neighbors fashion. There are already two problems
with this approach: audio signals are represented by high dimensional vectors
and the number of candidates can be very large - think thousands. The search
process would have a high complexity. Our paper will treat this problem through
hashing methodologies more specifically the Locality Sensitive Hashing. This
project will be in the spirit of classification and clustering problems. The
computer sound production principles will be used to determine which features
that describe an audio signal are the most useful. That will down-sample the
size of the feature vectors and speed up the process subsequently.
