Neuromorphic Coincidence Detector for Interaural Time Difference Encoding and Sound DOA Estimation

Eight decades ago, the medial superior olive (MSO) responsible for the extraction of the interaural time difference (ITD) was described as a combination of coincidence detection neurons and delay lines by Jeffress, a model largely used in spiking sound source localization systems. Coincidence detection mechanisms are also found in the visual system to encode speed of motion and time delays as described by the Hassenstein-Reichardt detector (HRD), a simple model of motion detection derived from behavioral observations in beetles. In this article, we propose an HRD-based model for ITD extraction of a binaural microphone pair from raw waveforms using neuromorphic components. This model is simulated in the computer language Python using equations correctly reflecting an analog neuromorphic technology. With a multilateration method, we estimate the direction of arrival (DOA) of click-like sounds recorded indoors with a three-microphone rectangular array at distances ranging between 24 cm and 10 m. A novel simplification of the mathematical equation is introduced for a rectangular array reducing by one order the polynomial characteristic expression giving the sound source’s 2-D coordinates. We report an encouraging DOA estimation accuracy of at least 84% (±5°) for sound sources located between 1 and 3 m away. The reported work expresses the potential for an ultralow-power consumption analog implementation of the proposed coincidence detector.