A neuromorphic microphone for sound localization
We propose a localization model which uses monaural spectral cues for localizing a sound source in a 1D plane. A neuromorphic microphone is constructed to implement this model; its operating principles take advantage of biologically-based monaural cues. The motivation is to understand human sound localization better and to build low-cost stereo capabilities into a single microphone. The head-related transfer function (HRTF) plays a critical role for human monaural localization since the shape of the external ear (pinna) spectrally shapes the sound differently for each sound source direction. Using HRTFs, humans can perceive the difference between front and back and the sound source's different elevation positions using one ear. The neuromorphic microphone relies on a specially shaped reflecting structure that allows echo-time processing to localize the sound. Since our recorded signal is composed of the direct sound and its echo, the sound is a simplified version of actual HRTF recordings which are composed of the direct sound and several reflections. The recorded signal is processed using a gamma filter. This generalizes the standard transversal filter by adding the ability to choose an optimal time-scale. The gamma filter solutions require on the order of five parameters while the more typical FIR filter solutions require hundreds of parameters. A multilayer perceptron is then used to learn the elevation angle of the sound, allowing the microphone to correctly localize sounds.