We are surrounded by sounds. Such a noisy environment makes it di?cult
to obtain desired speech and it is di?cult to converse comfortably
there. This makes it important to be able to separate and extract a
target speech signal from noisy observations for both man-machine and
human-human communication.
Blindsourceseparation(BSS)isanapproachforestimatingsourcesignals using
only information about their mixtures observed in each input channel.
The estimation is performed without possessing information on each
source, such as its frequency characteristics and location, or on how
the sources are mixed. The use of BSS in the development of comfortable
acoustic com- nication channels between humans and machines is widely
accepted. Some books have been published on BSS, independent component
ana- sis (ICA), and related subjects. There, ICA-based BSS has been well
studied in the statistics and information theory ?elds, for applications
to a variety of disciplines including wireless communication and
biomedicine. However, as speech and audio signal mixtures in a real
reverberant environment are generally convolutive mixtures, they involve
a structurally much more ch- lenging task than instantaneous mixtures,
which are prevalent in many other applications.