In this paper, we focus on the problem of content-based retrieval for
audio, which aims to retrieve all semantically similar audio recordings
for a given audio clip query. This problem is similar to the
problem of query by example of audio, which aims to retrieve media
samples from a database, which are similar to the user-provided example.
We propose a novel approach which encodes the audio into
a vector representation using Siamese Neural Networks. The goal is
to obtain an encoding similar for files belonging to the same audio
class, thus allowing retrieval of semantically similar audio. Using
simple similarity measures such as those based on simple euclidean
distance and cosine similarity we show that these representations can
be very effectively used for retrieving recordings similar in audio
content.