Aspinity’s new acoustic event detection evaluation kit with Infineon XENSIV™ MEMS microphone

Aspinity recently announced the availability of its Acoustic Event Detection Kit (EVK1) for battery-operated, smart home devices that are always listening for acoustic triggers. Such triggers can be window glass breaks, voice, or other acoustic events, delivering the essential technology that helps keep homes and families safe and secure.

The EVK1 features the company’s analogML™ core, a fully analog machine learning processor that promotes system power efficiency by identifying specific acoustic events before data digitization, Aspinity’s event detection algorithms, and Infineon’s new XENSIV™ IM73A135 high-performance, low-power analog MEMS microphone.

Traditional acoustic event detection devices are notoriously power-inefficient because they continuously monitor the environment. They 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 in a decade. Still, 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 demonstrates a power-saving alternative. Using an analogML core to detect acoustic events at the start of the signal chain while the microphone data are still analog. This way, the digital downstream system can remain in 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 accurately detect window glass breaks and run for years. You can go on vacation knowing that your home will be protected while you’re away. 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. It 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 now can now run much longer due to the reduced power consumption. Combining our high-performance XENSIV™ MEMS microphone with Aspinity’s analogML allows smart home devices to observe their environment continuously. The first signal analysis happens still in the analog domain,” said Dr. Roland Helm, VP and Head of PL Sensors, Infineon. “The analog-digital converter in the audio processing chain or the microcontroller will power up only when needed. 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.”

EVK1 Features

  • 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  

Availability

Aspinity’s EVK1 is currently sampling to key customers. For more information, email info@aspinity.com