Audio Localization for Robots Using Parallel Cerebellar Models
- Submitting institution
-
Liverpool Hope University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- MB11C
- Type
- D - Journal article
- DOI
-
10.1109/LRA.2018.2850447
- Title of journal
- IEEE Robotics and Automation Letters
- Article number
- -
- First page
- 3185
- Volume
- 3
- Issue
- 4
- ISSN
- 23773766
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- 12 - Engineering
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
S - Spatial Computing and Robotics (SC&R)
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work is significant as it publishes the first attempt to apply the adaptive filter model of the cerebellum to the auditory sense in robots, as well as the first attempt to apply a multiple models approach, developed in the context of motor control, to robot audition. The work has been cited in M. J. Mora-Regalado, O. Ruiz-Vivanco, A. González-Eras, and P. Torres-Carrión, "SMCS: Automatic Real-Time Classification of Ambient Sounds, Based on a Deep Neural Network and Mel Frequency Cepstral Coefficients", Springer International Publishing, 2020, pp. 245-253.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
