Codebook Based Audio Feature Representation For Music Information Retrieval
Vaizman Yonatan, Mcfee Brian, Lanckriet Gert. Arxiv 2013
[Paper]
ARXIV
Quantisation
Supervised
Digital music has become prolific in the web in recent decades. Automated
recommendation systems are essential for users to discover music they love and
for artists to reach appropriate audience. When manual annotations and user
preference data is lacking (e.g. for new artists) these systems must rely on
content based methods. Besides powerful machine learning tools for
classification and retrieval, a key component for successful recommendation is
the audio content representation.
Good representations should capture informative musical patterns in the audio
signal of songs. These representations should be concise, to enable efficient
(low storage, easy indexing, fast search) management of huge music
repositories, and should also be easy and fast to compute, to enable real-time
interaction with a user supplying new songs to the system.
Before designing new audio features, we explore the usage of traditional
local features, while adding a stage of encoding with a pre-computed
codebook and a stage of pooling to get compact vectorial
representations. We experiment with different encoding methods, namely
the LASSO, vector quantization (VQ) and cosine similarity
(CS). We evaluate the representations’ quality in two music information
retrieval applications: query-by-tag and query-by-example. Our results show
that concise representations can be used for successful performance in both
applications. We recommend using top-
Similar Work