The liquid voice sensor can detect these voices with an accuracy level of 99% in these nosey environments. The researchers were inspired by fatty organs located in the forehand of certain whale species such as sperm whales known as the "melon".
"The melon focuses and modulates vocalisations used in echolocation, matching the acoustic properties of its tissue to the surrounding water to allow sound to travel with minimal energy," Jun Chen, corresponding author of Nature Electronics which presented the sensor, told Tech Xplore.
Similar to the melon, the sensor could help reduce the loss of energy and minimise low-frequency noise, enabling the energy-efficient collection of acoustic data whilst improving its accuracy with its AI-powered sensing system that recognise sounds.
"Artificial intelligence plays a key role in our sensing system, specifically supporting voice recognition," explained Chen. "Thanks to the low-noise signals captured by the liquid acoustic sensor, the system achieves a high recognition rate with the support of deep learning algorithms."
The sensor developed by the researchers consists of a three-dimensional oriented and ramified magnetic network structure based on neodymium–iron–boron magnetic nanoparticles. These nanoparticles, which are suspended inside a carrier fluid, collectively behave as a magnet.
In initial tests, the liquid acoustic sensor developed by Chen and his colleagues was found to discriminate even minimal pressures of 0.9 Pa, while also exhibiting a high signal-to-noise ratio of 69.1 dB. Moreover, the sensor's self-filtering capabilities allowed it to reliably reduce low-frequency noise below 30 Hz originating from biomechanical movements.
In the future, the liquid sensor could be used to collect acoustic data underwater or in unusual environments, proving beneficial in environmental monitoring and marine operations.
