In this paper, the problem of the design of a simple and efficient music-speech discriminator for
large audio data sets in which advanced music playing techniques are taught and voice and music
are intrinsically interleaved is addressed. In the process, a number of features used in speech-music
discrimination are defined and evaluated over the available data set. Specifically, the data set
contains pieces of classical music played with different and unspecified instruments or even lyrics
and the voice of a teacher a top music performer or even the overlapped voice of the translator and
other persons. After an initial test of the performance of the features implemented, a selection
process is started, which takes into account the type of classifier selected beforehand, to achieve
good discrimination performance and computational efficiency, as shown in the experiments. The
discrimination application has been defined and tested on a large data set supplied by Fundación
Albéniz, containing a large variety of classical music pieces played with different instrument, which
include comments and speeches of famous performers.
