Smoke and mirrors &amp;#x2014; Virtual realities for sensor fusion experiments in biomimetic robotics

Bauer, Johannes; Dávila-Chacón, Jorge; Strahl, Erik; Wermter, Stefan

doi:10.1109/mfi.2012.6343022

Cited by 9 publications

(14 citation statements)

References 14 publications

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“…1 depicts our experimental setup. It consists of a humanoid robotic head immersed in a virtual reality (VR) setup designed for audio-visual integration [13]. The iCub is a platform designed for studies in embodied cognition and cognitive developmental robotics [14].…”

Section: Introductionmentioning

confidence: 99%

Neural and statistical processing of spatial cues for sound source localisation

Dávila-Chacón¹,

Magg²,

Liu

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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Abstract-When confronting binaural sound source localisation (SSL) algorithms with different environments and robotic platforms, there is an increasing need for non-linear integration methods of spatial cues. Based on interaural time and level differences, we compare the performance of several SSL systems. The architecture has three degrees of freedom, i.e. each tested architecture employs a different combination of representation of binaural cues, clustering and classification algorithms. The heuristic for the selection of methods is the same at each degree of freedom: to compare the impact of traditional statistical techniques versus machine learning algorithms with different degrees of biological inspiration. The overall performance is evaluated in the analysis of each system, including the accuracy of its output, training time and adequateness for life-long learning. The results support the use of hybrid systems, consisting different kinds of artificial neural networks, as they present an effective compromise between the characteristics evaluated.

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Section: Introductionmentioning

confidence: 99%

Neural and statistical processing of spatial cues for sound source localisation

Dávila-Chacón¹,

Magg²,

Liu

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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“…Both are detailed at the end of this section. Further details on the virtual environment and the parameters of the architecture can be found in [44] and [32].…”

Section: Bioinspired Computational Modelmentioning

confidence: 99%

“…The robots' ears consist of microphones perpendicular to the sagittal plane and are surrounded by pinnae. Further details about the VR setup and the principles that guide its design can be found in[44].…”

mentioning

confidence: 99%

Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization

Dávila-Chacón

Liu

Wermter

2019

IEEE Trans. Neural Netw. Learning Syst.

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Inspired by the behavior of humans talking in noisy environments, we propose an embodied embedded cognition approach to improve automatic speech recognition (ASR) systems for robots in challenging environments, such as with ego noise, using binaural sound source localization (SSL). The approach is verified by measuring the impact of SSL with a humanoid robot head on the performance of an ASR system. More specifically, a robot orients itself toward the angle where the signal-to-noise ratio (SNR) of speech is maximized for one microphone before doing an ASR task. First, a spiking neural network inspired by the midbrain auditory system based on our previous work is applied to calculate the sound signal angle. Then, a feedforward neural network is used to handle high levels of ego noise and reverberation in the signal. Finally, the sound signal is fed into an ASR system. For ASR, we use a system developed by our group and compare its performance with and without the support from SSL. We test our SSL and ASR systems on two humanoid platforms with different structural and material properties. With our approach we halve the sentence error rate with respect to the common downmixing of both channels. Surprisingly, the ASR performance is more than two times better when the angle between the humanoid head and the sound source allows sound waves to be reflected most intensely from the pinna to the ear microphone, rather than when sound waves arrive perpendicularly to the membrane.

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“…The experimental setup that we use is shown in Fig.3. The setup consists of a humanoid robot head (iCub) immersed in a display to create a virtual reality environment for the robot [25]. Speakers are located behind the display between 0°and 180°every 15°along the azimuth plane with the same elevation.…”

Section: A Datamentioning

confidence: 99%

EmoRL: Continuous Acoustic Emotion Classification Using Deep Reinforcement Learning

Lakomkin¹,

Zamani²,

Weber³

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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Acoustically expressed emotions can make communication with a robot more efficient. Detecting emotions like anger could provide a clue for the robot indicating unsafe/undesired situations. Recently, several deep neural network-based models have been proposed which establish new state-of-the-art results in affective state evaluation. These models typically start processing at the end of each utterance, which not only requires a mechanism to detect the end of an utterance but also makes it difficult to use them in a real-time communication scenario, e.g. human-robot interaction. We propose the EmoRL model that triggers an emotion classification as soon as it gains enough confidence while listening to a person speaking. As a result, we minimize the need for segmenting the audio signal for classification and achieve lower latency as the audio signal is processed incrementally. The method is competitive with the accuracy of a strong baseline model, while allowing much earlier prediction.

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Smoke and mirrors — Virtual realities for sensor fusion experiments in biomimetic robotics

Cited by 9 publications

References 14 publications

Neural and statistical processing of spatial cues for sound source localisation

Neural and statistical processing of spatial cues for sound source localisation

Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization

EmoRL: Continuous Acoustic Emotion Classification Using Deep Reinforcement Learning

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