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Subject:

Research Fellows in Audio Source Separation and Perceptual Audio (deadline: 24 Jan 2018)

From:

Mark Plumbley <[log in to unmask]>

Reply-To:

[log in to unmask]

Date:

Thu, 18 Jan 2018 11:59:38 +0000

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (119 lines)

Dear Multimedia Knowledge Management Network, 
Please forward the job opportunities below to anyone who may be interested... 
deadline approaching! Many thanks, Mark
----------------------------------------------------------------

Research Fellows in Audio Source Separation and Perceptual Audio
Evaluation

University of Surrey, UK
Salary:  GBP 30,688 to GBP 38,833 per annum  
Closing Date:  24 January 2018 (23:59 GMT)

Applications are invited for two Research Fellow posts:

(a) Research Fellow in Source Separation for Musical Audio Repurposing
https://jobs.surrey.ac.uk/107017

(b) Research Fellow in Perceptual Audio Evaluation 
https://jobs.surrey.ac.uk/107117

These are to work full-time on an EPSRC funded project "Musical Audio
Repurposing using Source Separation"
(https://cvssp.github.io/maruss-website/) from 1 February 2018 to 31
July 2018. This project is tackling the challenge of high quality
musical audio repurposing, focussing on soloing, desoloing, remixing and
upmixing. The project is investigating new methods for musical audio
source separation, in parallel with investigating new perceptual
evaluation measures for audio source separation.

For the Research Fellow in Source Separation for Musical Audio
Repurposing: The candidate will be responsible for investigating and
developing new and enhanced methods for high quality musical audio
source separation. These may include methods based on deep learning,
score-informed musical source separation, non-negative matrix
factorisation (NMF), and interactive methods employing user feedback.
The candidate will be working as part of a team, with other researchers
focussing on perceptual evaluation methods and software development of
open-source research tools.

The successful applicant is expected to have a PhD in electronic
engineering, computer science or a related subject, and is expected to
have research experience in audio signal processing and machine
learning. Research experience in one or more of the following is
desirable: audio source separation, audio upmixing, musical audio
processing, non-negative matrix factorization (NMF) and/or deep
learning. Experience in programming in one or more of Max/MSP, PureData,
Python, C++ and/or MATLAB will be an advantage, as will academic
publications in a relevant area.

For Research Fellow in Perceptual Audio Evaluation: The candidate will
be responsible for perceptually evaluating the results of musical audio
separation and repurposing. This will involve preparing and conducting
subjective experiments, analysing the results, and comparing these with
extant metrics and perceptual models. The candidate will make use of the
resulting data to either refine or develop new metrics or perceptual
models to aid the development and evaluation of musical source
separation and repurposing algorithms.

The successful applicant will have undertaken doctoral level research in
experimental/applied psychoacoustics or similar and will have expertise
in psychoacoustics, with an understanding of factors likely to be
related to source separation and audio repurposing. Skills in and
experience of the following are essential: the design and execution of
listening-based subjective experiments; and statistical analysis and
modeling. Also essential are strong writing skills for audiences of
varying levels of technical expertise, and the ability to work
independently and manage time effectively. A background in programming
in one or more of Max/MSP, PureData, Python, C++ and/or MATLAB will be
an advantage. Specific skills in source separation and audio repurposing
and the development of related perceptual models will also be an
advantage, as will academic publications in a relevant area.

The project is led by Prof Mark Plumbley in the Machine Audition Lab of
the Centre for Vision Speech and Signal Processing (CVSSP), and this
posts will be jointly supervised by Dr Russell Mason in the Institute of
Sound Recording (IoSR).

CVSSP (https://www.surrey.ac.uk/cvssp) is one of the largest groups of
its type in the UK, with over 120 active researchers working in the
areas of vision, image processing, and audio, and a grant portfolio of
over 12M. The Centre has state-of-the-art acoustic capture and
analysis facilities enabling research into audio source separation,
music transcription and spatial audio, and video and audio capture
facilities supporting research in real-time video and audio processing
and visualisation. The IoSR (http://iosr.surrey.ac.uk/) is a leading
centre for research in psychoacoustic engineering, as well as being home
to the Tonmeister undergraduate degree programme. It has a focused team
of 12 researchers, plus several industrial collaborators, and a range of
professional facilities of the highest standards, including three
recording studios and an ITU-R BS 1116 standard critical listening
room.

Informal enquires are welcome, to: Prof Mark Plumbley
([log in to unmask]) or Dr Russell Mason ([log in to unmask]).

For more information and to apply online, please visit: 

(a) Research Fellow in Source Separation for Musical Audio Repurposing
	https://jobs.surrey.ac.uk/107017
(b) Research Fellow in Perceptual Audio Evaluation
	https://jobs.surrey.ac.uk/107117

 We acknowledge, understand and embrace diversity.


--
Prof Mark D Plumbley
Professor of Signal Processing
Centre for Vision, Speech and Signal Processing (CVSSP) 
University of Surrey, Guildford, Surrey, GU2 7XH, UK
Email: [log in to unmask]

================================================================================
 LVA/ICA 2018
 14th International Conference on Latent Variable Analysis and Signal Separation
 July 2-6, 2018, University of Surrey, Guildford, UK
 http://cvssp.org/events/lva-ica-2018
 Paper submission deadline: February 4, 2018 (Full paper upload by February 7)
================================================================================

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