| EPSRC Reference: |
EP/G007144/1 |
| Title: |
Machine Listening using Sparse Representations |
| Principal Investigator: |
Plumbley, Professor M |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Sch of Electronic Eng & Computer Science |
| Organisation: |
Queen Mary University of London |
| Scheme: |
Leadership Fellowships |
| Starts: |
01 August 2008 |
Ends: |
30 September 2014 |
Value (£): |
1,461,379
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Digital Signal Processing |
| Vision & Senses - ICT appl. |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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26 Jun 2008
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Fellowship Allocation Panel Meeting
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Announced
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12 Jun 2008
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Fellowships 2008 Interviews - Panel D
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Deferred
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Summary on Grant Application Form |
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My aim for this Fellowship is to undertake a concerted programme of research in machine listening, the automatic analysis and understanding of sounds from the world around us. Through this research, and in collaboration with other international researchers, I aim to establish machine listening as a key enabling technology to improve our ability to interact with the world, leading to advances in many areas such as health, security and the creative industries.Human listeners have many capabilities a machine listening system should ideally have: to recognize a wide range of sounds; to segregate one sound source from a mixture of many sound sources; to judge complex attributes of sound such as rhythm and timbre (sound quality). Most human listeners take these abilities for granted, yet it has proved extremely difficult for conventional audio signal processing methods to tackle many of these tasks. Even currently successful tasks, such as automatic speech recognition, have typically led to very specialized techniques which cannot easily be applied to other domains. I propose to introduce new methods for machine listening of general audio scenes.As part of this work, I also will develop new interdisciplinary collaborations with both the machine vision and biological sensory research communities toinvestigate and develop general organizational principles for machine listening. One such principle that currently looks very promising is that of sparse representations. New theoretical advances and practical applications mean that sparse representations has recently emerged as a new and powerful analysis method, based on the principle that observations should be represented by only a few items chosen from a large number of possible items. This approach now has great potential for analysis and measurement of audio as well as other sensory signals. I also plan to use sparse representations to explore new biologically-inspired machine listening methods, and in turn to improve our understandingof biological hearing systems.Success in this research will open the way for new devices and systems able to process, identify and respond to a wide range of sounds, with diverse applications including: audio searching for the music and video industry; advances in hearing aids and cochlear implants; and incident detection for improved public safety on stations, roads and airports.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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