Connectionists: Fully funded PhD position on deep learning for musical structure analysis and generation

[Emmanuel Vincent]

Dear list,

We are offering a fully funded PhD position on deep learning for musical 
structure analysis and generation. Please note the tight application 
deadline (July 22). We will review applications on a continuous basis 
until then.


TITLE: Deep learning for musical structure analysis and generation
LAB: Inria Rennes & Inria Nancy, France
SUPERVISORS: Frédéric Bimbot & Emmanuel Vincent
STARTING DATE: October 2016 or later (until January 2017)
TO APPLY: send a CV, a motivation letter, a list of publications, and 
one or more recommendation letters to emmanuel.vincent at inria.fr as soon 
as possible and no later than July 22, 2016

ABOUT INRIA:
Inria is the biggest European public research institute dedicated to 
computer science. The PANAMA team (https://team.inria.fr/panama/) and 
the MULTISPEECH team (https://team.inria.fr/multispeech/) each gather 
20+ scientists with a focus on machine learning and signal processing 
for music, speech, and general audio.

RESEARCH TOPIC:
Despite numerous studies on automatic music transcription and 
composition, the temporal structure of music pieces at various time 
scales remains difficult to model. Automatic music improvisation systems 
such as OMax [1] and ImproteK [2] assume that the structure is either 
predetermined (chord chart) or completely free, which limits their use 
to specific musical styles. The concepts of semiotic structure [3] and 
Contrast & System [4] we recently introduced helped defining musical 
structure in a more general way. Yet, they do not easily translate into 
a computational model due to the large temporal horizon required and to 
the semantic gap with the observed musical signal or score. In the last 
few years, deep learning [5] has emerged as the new state of the art in 
the field of natural language processing (NLP) and it has already 
demonstrated its potential for modeling short-term musical structure [6, 7].

The goal of this PhD is to exploit and adapt deep recurrent neural 
networks (RNNs) for modeling medium- and long-term musical structure. 
This involves the following tasks in particular:
- designing new RNN architectures for jointly modeling music at several 
time scales: tatum, beat, bar, structural block (e.g., chorus or verse), 
whole piece,
- training them on smaller amounts of data than in the field of NLP,
- evaluating their performance for musical structure estimation and 
automatic music improvisation.

This position is part of a funded project with Ircam, in which the 
successful candidate will have the opportunity to engage.

PROFILE:
MSc in computer science, machine learning, or a related field.
Programming experience in Python or C/C++.
Previous experience with music and deep learning is not required but 
would be an asset.

REFERENCES:
[1] http://repmus.ircam.fr/omax/home
[2] http://improtekjazz.org/
[3] F. Bimbot, G. Sargent, E. Deruty, C. Guichaoua, E. Vincent, 
«Semiotic description of music structure: an introduction to the 
Quaero/Metiss structural annotations», in Proc. AES 53rd International 
Conference on Semantic Audio, 2014.
[4] F. Bimbot, E. Deruty, G. Sargent, E. Vincent, «System & Contrast : A 
polymorphous model of the inner organization of structural segments 
within music pieces», Music Perception, 2016.
[5] L. Deng, D. Yu, Deep Learning: Methods and Applications, Now 
Publishers, 2014.
[6] N. Boulanger-Lewandowski, Y. Bengio, P. Vincent, «Modeling temporal 
dependencies in high-dimensional sequences: Application to polyphonic 
music generation and transcription», in Proc. International Conference 
on Machine Learning (ICML), 2012.
[7] I.-T. Liu, B. Ramakrishnan, «Bach in 2014: Music composition with 
recurrent neural network», arXiv:1412.3191, 2014.

-- 
Emmanuel Vincent
Multispeech Project-Team
Inria Nancy - Grand Est
615 rue du Jardin Botanique, 54600 Villers-lès-Nancy, France
Phone: +33 3 8359 3083 - Fax: +33 3 8327 8319
Web: http://www.loria.fr/~evincent/