Jan Schlüter, Reinhard Sonnleitner,
"Unsupervised Feature Learning for Speech and Music Detection in Radio Broadcasts."
: Proceedings of the 15th Int. Conference on Digital Audio Effects (DAFx-12),, 9-2012
Unsupervised Feature Learning for Speech and Music Detection in Radio Broadcasts.
Sprache des Titels:
Proceedings of the 15th Int. Conference on Digital Audio Effects (DAFx-12),
Detecting speech and music is an elementary step in extracting information
from radio broadcasts. Existing solutions either rely on
general-purpose audio features, or build on features specifically
engineered for the task. Interpreting spectrograms as images, we
can apply unsupervised feature learning methods from computer
vision instead. In this work, we show that features learned by a
mean-covariance Restricted Boltzmann Machine partly resemble
engineered features, but outperform three hand-crafted feature sets
in speech and music detection on a large corpus of radio recordings.
Our results demonstrate that unsupervised learning is a powerful
alternative to knowledge engineering.