Piezoelectric nanofibers-based intelligent hearing system

Hearing loss, affecting individuals of all ages, can impair education, social function and quality of life. Current treatments, such as hearing aids and implants, aim to mitigate these effects but often fall short in addressing the critical issue of accurately pinpointing sound sources. We report an intelligent hearing system inspired by the human auditory system: an asymmetric well-aligned piezoelectric nanofibres combined with neural networks to mimic natural auditory processes. Piezoelectric nanofibers with spirally varying lengths and directions transmit and convert acoustic sound into mechanoelectrical signals, mimicking the complex cochlear dynamics. These signals are then encoded by digital neural networks, enabling accurate sound direction recognition. This intelligent hearing system surpasses human directional hearing, accurately recognising sound directions horizontally and vertically. The advancement represents a significant stride towards next-generation artificial hearing, harmonising transduction and perception with a nature-inspired design. It promises for applications in hearing aids, wearable devices and implants, offering enhanced auditory experiences for those with hearing impairments. This dataset contains essential data collected from the piezoelectric nanofibers and the intelligent hearing system.

Methods
In this study, we explore the dynamic piezo-acoustic response of piezoelectric nanofibers for an intelligent hearing system. Our investigation captures the intricate behaviours of these materials when exposed to various acoustic stimuli, revealing their potential in advanced sensory applications. Here, we detail our methodology, original data and key findings related to piezoelectric force responses, signal processing techniques, and directional recognition capabilities, emphasizing the insights derived from our experiments.

The data demonstrated original piezoelectric force responses in the local area of a single nanofiber. The piezo-acoustic signal was collected directly from the device. Examples of original piezo-acoustic transduction were also included. The piezo-acoustic signal was preprocessed using a Short-Time Fourier Transform (STFT). The tonotopic profile of the device was further analyzed to examine the frequency features of the piezo-acoustic response and correlate them with signal channel frequency features.

For directional recognition, the preprocessed original dataset was attached. The data was segmented for input and analyzed for accuracy calculation. The regression-based self-learning recognition demonstrated its capability to recognize unknown directions.