Simple Ears Inspire Frequency Agility in an Engineered Acoustic Sensor System

Abstract

Abstract-Standard microphones and ultrasonic devices are generally designed with a static and flat frequency response in order to address multiple acoustic applications. However, they may not be flexible or adaptable enough to deal with some requirements. For instance, when operated in noisy environments such devices may be vulnerable to wideband background noise which will require further signal processing techniques to remove it, generally relying on digital processor units. In this work, we consider if microphones and ultrasonic devices could be designed to be sensitive only at selected frequencies of interest, whilst also providing flexibility in order to adapt to different signals of interest and to deal with environmental demands. This research exploits the concept where the "transducer becomes part of the signal processing chain" by exploring feedback processes between mechanical and electrical mechanisms that together can enhance peripheral sound processing. This capability is present within a biological acoustic system, namely in the ears of certain moths. That was used as the model of inspiration for a smart acoustic sensor system which provides dynamic adaptation of its frequency response with amplitude and time dependency according to the input signal of interest.

Towards the development of a frequency agile MEMS acoustic sensor system

“…As reported in the previous study [5], the simulated and measured acoustic frequency response of such a transducer can be altered by increasing its equivalent stiffness. Here, the stiffness increment effect over the microphone's diaphragm was tested by driving the capacitive port of the device with DC voltage potentials between 0 to 50 V with 5 V step increments.…”

“…On the other hand, without any input stimuli the embedded system sets the bias voltage to return back to its initial value. Overall, the acoustic frequency response of this new sensor system (sensed by the piezoelectric port of the MEMS microphone) is changed dynamically as a function of the applied input sound level (acoustic stimuli) as similarly reported in the previous work [5].…”

“…4. Moreover, in order to evaluate the adaptive capabilities of this new device, it was placed in an embedded feedback loop system computing a threshold based algorithm to enable frequency agility of the front-end sensor (refer to [5] for complete description about the embedded system setup and algorithm used). Once a pre-defined threshold is reached, the embedded system automatically updates the bias voltage to the MEMS microphone (driving the rotor combs of the device) that consequently alters its acoustic frequency response.…”

“…Designing acoustic sensors such as MEMS microphones, which are inspired by nature is not new [2][3][4]. Moreover, a novel acoustic bio-inspired concept was recently presented in [5] where the "transducer becomes part of the signal processing chain". It can be achieved by exploring feedback mechanisms at the sensor level in order to alter its mechanical response and consequently its acoustic frequency response can be changed.…”

Enhancing Acoustic Sensory Responsiveness by Exploiting Bio-inspired Feedback Computation

“…Conventional transducer design techniques may set physical constraints on the sensor or system responses, which thus result in limited or static values of the Q-factor and 0 for a desired front-end acoustic system response. However, studies have already shown that the Q-factor of some sensors can be manipulated through the use of feedback control mechanisms [6][7], and that the 0 can also be changed dynamically by exploiting feedback system approaches in a similar manner [8]. Interestingly, some biological sensors and systems such as those involved in the process of hearing, are thought to operate with feedback control techniques in order to dynamically adapt their front-end acoustic responsesthe process known as active hearing [9][10].…”