New Acoustic Event Detection evaluation kit highlights Aspinity’s ultra-low-power, programmable AnalogML™ core and Infineon’s newest analog MEMS microphone.
Targeting the growing market for smart home devices—which IDC estimates will grow to more than 1.4B units in 20241 — Aspinity announced the availability of its Acoustic Event Detection Kit (EVK1) for battery-operated, smart home devices that are always listening for acoustic triggers such as window glass breaks, voice, or other acoustic events—delivering the essential technology that helps keep homes and families safe and secure. Featuring the company’s analogML™ core—a fully analog machine learning processor that promotes system power efficiency by identifying specific acoustic events prior to data digitization—along with Aspinity’s event detection algorithms and Infineon’s new XENSIV™ IM73A135 high-performance, low-power analog MEMS microphone—the EVK1 is a complete hardware-software kit for the development of small, always-listening smart home devices with extended battery lifetimes.
Traditional acoustic event detection devices are notoriously power-inefficient because they continuously monitor the environment and immediately digitize all microphone data for analysis—even though most of that data are simply noise. A window glass break, for example, may only happen once a decade but the typical glass break sensor uses high-power digital analysis of 100% of the ambient sound data to detect a trigger that rarely (or never) occurs. Aspinity’s EVK1, on the other hand, demonstrates a power-saving alternative. By using an analogML core to detect acoustic events at the start of the signal chain while the microphone data are still analog, the downstream digital system can remain in an ultra-low-power sleep mode until an event is detected. This architectural approach allows designers to build acoustic event detection devices with batteries that last years, instead of months, on a single charge.
“Stoked by demand for smarter real-time monitoring of potential dangers in the home, the market for acoustic event detection in battery-powered smart home devices is exploding,” said Tom Doyle, founder and CEO, Aspinity. “Such devices help people feel safer and more secure, whether they’re home or away, which is why it’s so important to keep them up and running for extended periods. Our EVK1 makes it easy to develop small devices that can very accurately detect window glass break and run for years, so you can go on vacation knowing that your home will be protected while you’re away—and you’ll be spared those annoying phone calls on false alarms triggered by other loud sounds in the neighborhood.”
The EVK1 features Infineon’s ultra-high performance XENSIV™ IM73A135 MEMS microphone for accurate, real-time monitoring of acoustic events and reflects another step forward in the ongoing partnership between Aspinity and Infineon Technologies AG. (See: Aspinity and Infineon partner to accelerate development of intelligent sensing products with longer lasting batteries, May 14, 2020.)
“Always listening battery-driven devices can now run much longer due to the reduced power consumption. The combination of our high-performance XENSIV™ MEMS microphone with Aspinity’s analogML allows a continuous observation of the environment and a first signal analysis still in the analog domain. The analog-digital converter in the audio processing chain or the microcontroller will power up only when needed,” said Dr. Roland Helm, VP and head of PL Sensors, Infineon Technologies AG. “That’s a major competitive advantage for designers. We’re delighted to collaborate with Aspinity to speed development of power-efficient smart home devices that are always listening for meaningful acoustic events without sacrificing battery life.”
AnalogML™ core—a programmable, analog machine learning processor that uses near-zero-power to detect acoustic or other sensor events in analog sensor data
Infineon’s XENSIV™ IM73A135 high-performance analog MEMS microphone—a 73 dB SNR analog MEMS microphone with a power consumption of just 170 μA
Aspinity algorithms—easy to load onto analogML core for acoustic detection of window glass break or voice, with additional acoustic event detection algorithms coming soon
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