PhD thesis announcement (Mark Every )

Subject: PhD thesis announcement
From:    Mark Every  <m.every@xxxxxxxx>
Date:    Fri, 10 Mar 2006 14:55:52 +0000

Please find my recently completed PhD thesis entitled "Separation of
Musical Sources and Structure from Single-Channel Polyphonic
Recordings", and demonstrations at:

http://www.ee.surrey.ac.uk/Personal/M.Every/

Regards,
Mark Every

ABSTRACT:

The thesis deals principally with the separation of pitched sources from
single-channel polyphonic musical recordings. The aim is to extract from
a mixture a set of pitched instruments or sources, where each source
contains a set of similarly sounding events or notes, and each note is
seen as comprising partial, transient and noise content. The work also
has implications for separating non-pitched or percussive sounds from
recordings, and in general, for unsupervised clustering of a list of
detected audio events in a recording into a meaningful set of source
classes. The alignment of a symbolic score/MIDI representation with the
recording constitutes a pre-processing stage. The three main areas of
contribution are: firstly, the design of harmonic tracking algorithms
and spectral-filtering techniques for removing harmonics from the
mixture, where particular attention has been paid to the case of
harmonics which are overlapping in frequency. Secondly, some studies
will be presented for separating transient attacks from recordings, both
when they are distinguishable from and when they are overlapping in time
with other transients. This section also includes a method which
proposes that the behaviours of the harmonic and noise components of a
note are partially correlated. This is used to share the noise component
of a mixture of pitched notes between the interfering sources. Thirdly,
unsupervised clustering has been applied to the task of grouping a set
of separated notes from the recording into sources, where notes
belonging to the same source ideally have similar features or
attributes. Issues relating to feature computation, feature selection,
dimensionality and dependence on a symbolic music representation are
explored. Applications of this work exist in audio spatialisation, audio
restoration, music content description, effects processing and
elsewhere.