Information and neural dynamics in the perception of musical structure
Lead Research Organisation:
Queen Mary University of London
Department Name: Sch of Electronic Eng & Computer Science
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Publications
Abdallah S
(2010)
Comment on "Unified View of Prediction and Repetition Structure in Audio Signals With Application to Interest Point Detection
in IEEE Transactions on Audio, Speech, and Language Processing
Abdallah S
(2012)
A measure of statistical complexity based on predictive information with application to finite spin systems
in Physics Letters A
Abdallah S
(2012)
Cognitive music modelling: An information dynamics approach
Abdallah S
(2013)
Geometric Science of Information
H. Ekeus
(2012)
The melody triangle: Exploring pattern and predictability in music
in N/A
Henrik Ekeus (Author)
(2012)
The Melody Triangle: Exploring pattern and predictability in music
K. Agres
(2013)
An information-theoretic account of musical expectation and memory
in N/A
Peter Foster (Author)
(2011)
Causal prediction of continuous-valued music features
Samer A. Abdallah (Author)
(2012)
Predictive Information in Discrete-time Gaussian Processes
Samer A. Abdallah (Author)
(2010)
Predictive information, multi-information, and binding information
Samer A. Plumbley (Author)
(2012)
Predictive information and Bayesian surprise in exchangeable random processes
| Description | We proposed a new information-theoretic measure of the complexity between random variables, called "binding information", which is sensitive to the presence of higher-order interactions. We related this to other information-theoretic measures such as entropy, predictive information and erasure entropy. For binary random variables, binding information is maximized by a parity process. We also extended our previous work on symbolic predictive information rate (PIR) to real-valued signals such as autoregressive Gaussian processes AR(N) and moving average processes MA(N). We showed that PIR is unbounded for MA(N) processes. Applying this to analysis of musical audio (Steve Reich: Drumming), we found that Bayesian surprise and predictive information rate correspond to significant events in the score and structural features of the music. We introduced a music composition tool based on searching a space with varying predictive information rate, formed as a trade-off between periodicity, repetition and noise. This tool, the "Melody triangle", was implemented firstly within a room using a Kinect motion sensor, and subsequently as an Android smartphone app. The Melody triangle allows people to indicate which melodies they "like", allowing us to survey user preferences and compare against our information measures. |
| Exploitation Route | In music, the measures of information applied to music could give music composers and listeners new insights into the structure of music that they work with. It could also lead to accessible adaptive composition tools allowing users to select the complexity of music they prefer. By matching preference to musical experience of listeners, it could also be used as part of music recommendation systems. Other potential applications include improved prediction models for music coding and compression. |
| Sectors | Creative Economy,Digital/Communication/Information Technologies (including Software) |
| Description | The Melody Triangle Android App was released to allow users to create music using the concepts from this project, and has been installed over 1000 times. For more details see http://melodytriangle.eecs.qmul.ac.uk/ |
| First Year Of Impact | 2013 |
| Sector | Digital/Communication/Information Technologies (including Software),Leisure Activities, including Sports, Recreation and Tourism |
| Impact Types | Cultural |
| Description | Platform Grant: Digital Music |
| Amount | £1,161,334 (GBP) |
| Funding ID | EP/K009559/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2013 |
| End | 01/2018 |
| Description | Workshop on Information Dynamics of Music (IDyOM) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Other audiences |
| Results and Impact | A one-day workshop providing a forum for dissemination and discussion of cutting edge research on dynamic predictive processing of musical structure in: probabilistic and information-theoretic models; cognitive, psychological and neural processing; musicological analysis. |
| Year(s) Of Engagement Activity | 2013 |
| URL | http://www.eecs.qmul.ac.uk/events/view/workshop-on-information-dynamics-of-music-idyom2 |