A Machine Hearing System for Binaural Sound Localization based on Instantaneous Correlation

Xu, Ying; Afshar, Saeed; Singh, Ram Kuber; Hamilton, Tara Julia; Wang, Runchun; Schaik, André van

doi:10.1109/iscas.2018.8351367

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…The CAR-FAC model has been shown to provide noise-robust features in audio to perform speaker identification (Islam et al, 2022). Audio features from the CAR-FAC cochlea model have also been used to perform noise-robust binaural sound localisation (Xu et al, 2018a(Xu et al, , 2019(Xu et al, , 2021.…”

Section: Discussionmentioning

confidence: 99%

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Perera

Bethi

et al. 2023

Front. Neurosci.

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This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.

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Section: Discussionmentioning

confidence: 99%

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Perera

Bethi

et al. 2023

Front. Neurosci.

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“…The CT voltage-domain filter discussed in Section IV-B is combined with a rectifier and a spike generation stage to form a cochlea channel leading to the multi-channel Dynamic Audio Sensor (DAS) silicon cochlea [6]. This design has been used in applications such as sound source localization [15], [66] using the spike timing of the binaural spikes from the DAS. It was also used for multi-modal recognition [18], [67], and keyword spotting using deep neural networks (DNNs) [16], [19].…”

Section: Discussionmentioning

confidence: 99%

Continuous-Time Analog Filters for Audio Edge Intelligence: Review and Analysis on Design Techniques

Kim¹,

Liu²

2022

Preprint

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Silicon cochlea designs capture the functionality of the biological cochlea. Their use has been explored for cochlea prosthesis applications and more recently in edge audio devices which are required to support always-on operation. As their stringent power constraints pose several design challenges, IC designers are forced to look for solutions that use low standby power. One promising bio-inspired approach is to combine the continuous-time analog filter channels of the silicon cochlea with a small memory footprint deep neural network that is trained on edge tasks such as keyword spotting, thereby allowing all blocks to be embedded in an IC. This paper reviews the analog filter circuits used as feature extractors for current edge audio devices, starting with the original biquad filter circuits proposed for the silicon cochlea. Our analysis starts from the interpretation of a basic biquad filter as a two-integrator-loop topology and reviews the progression in the design of second-order low-pass and band-pass filters ranging from OTA-based to source-followerbased architectures. We also derive and analyze the small-signal transfer function and discuss performance aspects of these filters. The analysis of these different filter configurations can be applied to other application domains such as biomedical devices which employ a front-end bandpass filter.

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“…The model has been trained using spike-timing-dependent plasticity (STDP) based on ear canal recordings of a domestic cat acquired in a sound-dampened chamber. Similarly, Xu et al [18] utilized a correlational approach based on ITD-related onset-timing features extracted from an auditory filter device mimicking cochlear processing. Using sound samples from speakers in a reverberant environment the extracted features have been used in either a regression or extreme learning machine (ELM) approach for SSL.…”

Section: Related Workmentioning

confidence: 99%

Listen to the Brain–Auditory Sound Source Localization in Neuromorphic Computing Architectures

Schmid

Oess

Neumann

2023

Sensors

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Conventional processing of sensory input often relies on uniform sampling leading to redundant information and unnecessary resource consumption throughout the entire processing pipeline. Neuromorphic computing challenges these conventions by mimicking biology and employing distributed event-based hardware. Based on the task of lateral auditory sound source localization (SSL), we propose a generic approach to map biologically inspired neural networks to neuromorphic hardware. First, we model the neural mechanisms of SSL based on the interaural level difference (ILD). Afterward, we identify generic computational motifs within the model and transform them into spike-based components. A hardware-specific step then implements them on neuromorphic hardware. We exemplify our approach by mapping the neural SSL model onto two platforms, namely the IBM TrueNorth Neurosynaptic System and SpiNNaker. Both implementations have been tested on synthetic and real-world data in terms of neural tunings and readout characteristics. For synthetic stimuli, both implementations provide a perfect readout (100% accuracy). Preliminary real-world experiments yield accuracies of 78% (TrueNorth) and 13% (SpiNNaker), RMSEs of 41∘ and 39∘, and MAEs of 18∘ and 29∘, respectively. Overall, the proposed mapping approach allows for the successful implementation of the same SSL model on two different neuromorphic architectures paving the way toward more hardware-independent neural SSL.

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A Machine Hearing System for Binaural Sound Localization based on Instantaneous Correlation

Cited by 8 publications

References 19 publications

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Continuous-Time Analog Filters for Audio Edge Intelligence: Review and Analysis on Design Techniques

Listen to the Brain–Auditory Sound Source Localization in Neuromorphic Computing Architectures

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