A biologically inspired spiking neural network model of the auditory midbrain for sound source localisation

Liu, Jindong; Pérez-González, David; Rees, Adrian; Erwin, Harry; Wermter, Stefan

doi:10.1016/j.neucom.2009.10.030

Cited by 26 publications

(40 citation statements)

References 12 publications

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“…The optimised algorithm proved capable of segregating sound sources with similar precision to state-of-the-art algorithms [3,10,9]. Estimating the optimal max ILD value for Nao's head allowed to double the resolution for localisation in comparison to Liu et al [2]. The max ILD was found through a statistical analysis of the LSO activation across its frequency components.…”

Section: Discussionmentioning

confidence: 99%

“…The max ILD was found through a statistical analysis of the LSO activation across its frequency components. Frequency decomposition opens the possibility of localising concurrent and dynamic sound sources [2]. Such advantage lacks in networks learning ITD pairs directly extracted from the sound wave cross correlation.…”

Section: Discussionmentioning

confidence: 99%

“…For SSL with humanoid dummy heads [2] it is possible to obtain the value of max ILD from literature. However, for Nao's head it was necessary to estimate the max ILD from the statistics of the LSO activation.…”

Section: Computational Modelmentioning

confidence: 99%

“…A meta-objective of artificial SSL systems is their portability to different robots. This paper describes the design and implementation of Liu et al [2] algorithm for sound localisation on a Nao robotic platform with ∼40 dB of ego-noise.…”

Section: Introductionmentioning

confidence: 99%

“…The results from these studies have provided valuable insights on the design requirements of a biomimetic SSL system. Finally, Liu et al [2] implemented models of the MSO, LSO and IC using SNNs. Connections from the MSO and LSO to the IC were estimated using Bayesian inference.…”

Section: Introductionmentioning

confidence: 99%

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Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation

Dávila-Chacón

Heinrich

Liu

et al. 2012

Artificial Neural Networks and Machine Learning – ICANN 2012

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Abstract. This paper presents a spiking neural network (SNN) for binaural sound source localisation (SSL). The cues used for SSL were the interaural time (ITD) and level (ILD) differences. ITDs and ILDs were extracted with models of the medial superior olive (MSO) and the lateral superior olive (LSO). The MSO and LSO outputs were integrated in a model of the inferior colliculus (IC). The connection weights between the MSO and LSO neurons to the IC neurons were estimated using Bayesian inference. This inference process allowed the algorithm to perform robustly on a robot with ∼40 dB of ego-noise. The results showed that the algorithm is capable of differentiating sounds with an accuracy of 15 • .

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Computational Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Biomimetic Binaural Sound Source Localisation with Ego-Noise Cancellation

Dávila-Chacón

Heinrich

Liu

et al. 2012

Artificial Neural Networks and Machine Learning – ICANN 2012

Self Cite

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Improving Humanoid Robot Speech Recognition with Sound Source Localisation

Dávila-Chacón

Twiefel

Liu

et al. 2014

Artificial Neural Networks and Machine Learning – ICANN 2014

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Abstract. In this paper we propose an embodied approach to automatic speech recognition, where a humanoid robot adjusts its orientation to the angle that increases the signal-to-noise ratio of speech. In other words, the robot turns its face to 'hear' the speaker better, similar to what people with auditory deficiencies do. The robot tracks a speaker with a binaural sound source localisation system (SSL) that uses spiking neural networks to model relevant areas in the mammalian auditory pathway for SSL. The accuracy of speech recognition is doubled when the robot orients towards the speaker in an optimal angle and listens only through one ear instead of averaging the input from both ears.

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