Applications are invited 1 fully-funded PhD studentship, allied to the
5 year EPSRC and Digital Economy funded Programme Grant: Fusing Audio
and Semantic Technologies for Intelligent Music Production and
Consumption (FAST-IMPACt or FAST - see www.semanticaudio.ac.uk).
FAST-IMPACt aims to answer questions such as: How can next generation
web technologies (Ontologies, Linked Data, Metadata) combined with
music content analysis in the studio bring new value and functionality
to producers, creators, consumers and intermediaries of music content?
And how will both ends of the music value chain benefit from more
engaging interactions (enhanced productivity, increased enjoyment and
immersion) while creating or consuming music? And can intermediaries
add value with semantically enhanced services?
Helping us pursue this vision are national and international partners
from academia and industry, including BBC R&D, Abbey Road, Solid State
Logic, International Audio Labs and more.
Candidates must have a first-class honours degree or equivalent, or a
good MSc Degree in Computer Science, Electronic Engineering, Sound &
Music Computing or equivalent. Candidates should be confident in
digital signal processing and/or machine learning, and have
programming experience in, e.g. MATLAB, Mathematica, Python, Java, C++
or similar. Experience in research and a track record of publications
is very advantageous. Formal music training or sound engineering
experience is also advantageous.
Positions are available immediately. Only 1 place is available with
full fees and stipend; but additional positions may be available for
self-funded or part-funded applicants. Please apply online via the
Queen Mary University of London application system, quoting the
specific project(s) of interest. Enquiries may be addressed to
mark.sandler@qmul.ac.uk.
Projects titles are below. Fuller details of each of the projects
below are available at http://tinyurl.com/jye2x69
[SAMI1] Studio Science: improving feature extraction in the studio;
delivering new experiences to the consumer
[SAMI2] Enhancing the music listening experience
[SAMI3] Song level audio features for navigating large music collections
[SAMI4] Note level audio features for understanding and visualising
musical performance
[SAMI5] Audio features for MIR based on human hearing physiology and
neuroscience and on acoustics
[SAMI6] Compression of individual instrument stems for compact
multi-track audio formats
--
professor mark sandler, CEng, FIEEE, FAES, FIET, FBCS
royal society wolfson research merit award holder
director of the EPSRC/AHRC CDT in media and arts technology (MAT)
director of the centre for digital music (c4dm)
school of electronic engineering and computer science
queen mary university of london
mark.sandler@qmul.ac.uk
+44 (0)20 7882 7680
+44 (0)7775 016715
twitter: @markbsandler,
follow the FAST-IMPACt Programme Grant @semanticaudio
