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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">A <b class="">fully-funded PhD studentship</b> is available to carry out research in the area of <b class="">Optical Music Recognition
using Deep Learning</b> in collaboration with Steinberg Media Technologies GmbH.</span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">The position is available within the <b class="">UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM) at
Queen Mary University of London.</b></span></div>
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<span class="" style="text-decoration: underline; -webkit-font-kerning: none;"><a href="https://www.aim.qmul.ac.uk/" class="">https://www.aim.qmul.ac.uk/</a></span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">The studentships covers fees and a stipend for four years starting September 2019. </span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">The position is open to UK and international students. </span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Application deadline: 21 June 2019 (</span><span class="" style="-webkit-font-kerning: none;"><a href="http://www.aim.qmul.ac.uk/apply" class="">http://www.aim.qmul.ac.uk/apply</a>)</span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;"><b class="">Why apply to the AIM Programme?</b></span></div>
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<li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">4-year fully-funded PhD studentships available</span></li><li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Access to cutting-edge facilities and expertise in artificial intelligence (AI) and music/audio technology</span></li><li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Comprehensive technical training at the intersection of AI and music through a personalised programme</span></li><li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Partnerships with over 20 companies and cultural institutions in the music, audio and creative sectors</span></li></ul>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">More information on the AIM Programme can be found at: <a href="https://www.aim.qmul.ac.uk/" class=""><span class="" style="-webkit-font-kerning: none;">https://www.aim.qmul.ac.uk/</span></a></span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;"><b class="">PhD Topic: Optical Music Recognition using Deep Learning </b>in collaboration with Steinberg Media Technologies GmbH.</span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">The proposed PhD focuses on developing novel techniques for optical music recognition (OMR) using Deep Neural Networks (DNN). The
research will be carried out in collaboration with Steinberg Media Technologies opening the opportunity to work with and test the research outcomes in leading music notation software such as Dorico (<a href="http://www.dorico.com" class="">http://www.dorico.com</a>). </span>Musicians,
composers, arrangers, orchestrators and other users of music notation have long had a dream that they could simply take a photo or use a scan of sheet music and bring it into a music notation application to be able to make changes, rearrange, transpose, or
simply listen to being played by the computer. The PhD aims to investigate and demonstrate a novel approach to converting images of sheet music into a semantic representation such as MusicXML and/or MEI. The research will be carried out in the context of designing
a music recognition engine capable of ingesting, optically correcting, processing and recognising multiple pages of handwritten or music from image captured by mobile phone, or low-resolution copyright-free scans from the International Music Score Library
Project (IMSLP). The main objective is outputting semantic mark-up identifying as many notational elements and text as possible, along with the relationship to their position in the original image. Prior solutions have used algorithmic approaches and have
involved layers of algorithmic rules applied to traditional feature detection techniques such as edge detection. An opportunity exists to develop and evaluate new approaches based on DNN and other machine learning techniques. State-of-the-art Optical Music
Recognition (OMR) is already able to recognise clean sheet music with very high accuracy, but fixing the remaining errors may take just as long, if not longer, than transcribing the music into notation software by hand. A new method that can improve recognition
rates will allow users who are not so adept at inputting notes into a music notation application to get better results quicker. Another challenge to tackle is the variability in quality of input (particularly from images captured from smartphones) and how
best to preprocess the images to improve the quality of recognition for subsequent stages of the pipeline. The application of cutting edge techniques in data science, including machine learning, particularly convolutional neural networks (CNN) may yield better
results than traditional methods. To this end, research will start from testing VGG like architectures (<a href="https://arxiv.org/abs/1409.1556" class="">https://arxiv.org/abs/1409.1556</a>) and residual networks (e.g. ResNet, <a href="https://arxiv.org/pdf/1512.03385.pdf" class="">https://arxiv.org/pdf/1512.03385.pdf</a>)
for the recognition of hand written and/or low-resolution printed sheet music. The same techniques may also prove useful in earlier stages of the pipeline such as document detection and feature detection. It would be desirable to recognise close to all individual
objects in the score. One of the first objectives will be to establish the methodology for determining the differences between the reference data and the recognised data. Furthermore data augmentation can be supported by existing Steinberg software. The ideal
candidate would have previous experience of training machine learning models and would be familiar with Western music notation. Being well versed in image acquisition, processing techniques, and computer vision would be a significant advantage. </div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;"><b class="">Programme structure</b></span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Our Centre for Doctoral Training (CDT) offers a four year training programme where students will carry out a research project in
the intersection of AI and music, supported by taught specialist modules, industrial placements, and skills training. Find out more about the programme structure at: <a href="http://www.aim.qmul.ac.uk/about/" class=""><span class="" style="-webkit-font-kerning: none;">http://www.aim.qmul.ac.uk/about/</span></a></span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;"><b class="">Who can apply?</b></span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">We are on the lookout for the best and brightest students interested in the intersection of music/audio technology and AI. Successful
applicants will have the following profile:</span></div>
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<li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Hold or be completing a Masters degree at distinction or first class level, or equivalent, in Computer Science, Electronic Engineering,
Music/Audio Technology, Physics, Mathematics, or Psychology.</span></li><li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Programming skills are strongly desirable; however we do not consider this to be an essential criterion if candidates have complementary
strengths.</span></li><li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Formal music training is desirable, but not a prerequisite.</span></li><li class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">This position is open to UK and international students. </span></li></ul>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;"><b class="">Funding</b></span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Funding will cover the cost of tuition fees and will provide an annual tax-free stipend of £17,009. The CDT will also provide funding
for conference travel, equipment, and for attending other CDT-related events.</span></div>
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<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;"><b class="">Apply Now</b></span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Information on applications and PhD topics can be found at: <a href="http://www.aim.qmul.ac.uk/apply" class=""><span class="" style="-webkit-font-kerning: none;">http://www.aim.qmul.ac.uk/apply</span></a></span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">Application deadline: <b class="">21 June 2019</b></span></div>
<div class="" style="margin: 0px; font-stretch: normal; line-height: normal;"><span class="" style="-webkit-font-kerning: none;">For further information on eligibility, funding and the application process please visit our website. Please email any questions
to <a href="mailto:aim-enquiries@qmul.ac.uk" class=""><span class="" style="-webkit-font-kerning: none;">aim-enquiries@qmul.ac.uk</span></a></span></div>
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<div class="">— </div>
<div class="">Dr. George Fazekas, </div>
<div class="">Senior Lecturer (Assoc. Prof.) in Digital Media </div>
<div class="">Programme Coordinator, <a href="http://bit.ly/smc-qmul" class="">Sound and Music Computing (SMC)</a></div>
<div class="">Centre for Digital Music (C4DM)</div>
<div class="">School of Electronic Engineering and Computer Science</div>
<div class="">Queen Mary University of London, UK</div>
<div class="">FHEA, M. IEEE, ACM, AES</div>
<div class="">email: <a href="mailto:g.fazekas@qmul.ac.uk" class="">g.fazekas@qmul.ac.uk</a></div>
<div class="">web: <a href="http://c4dm.eecs.qmul.ac.uk" class="">c4dm.eecs.qmul.ac.uk</a> | <a href="http://semanticaudio.net" class="">semanticaudio.net</a> | <a href="http://audiocommons.org/" class="">audiocommons.org</a> | <a href="http://bit.ly/smc-qmul" class="">bit.ly/smc-qmul</a> | <a href="http://aim.qmul.ac.uk" class="">aim.qmul.ac.uk</a></div>
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