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Knowles Launches Raspberry Pi Development Kit to Enable Voice Integration for New IoT Applications and Industries

Knowles Corporation, a market leader and global provider of advanced micro-acoustic microphones and speakers, audio processing, and precision device solutions, announced the availability of the Knowles AISonic IA8201 Raspberry Pii Development Kit to bring voice, audio edge processing, and machine learning (ML) listening capabilities to devices and systems in a range of new industries. By leveraging the kit, product designers and engineers from OEM/ODM companies have a single tool to streamline design, development, and testing of technology that pushes the boundaries of voice and audio integration in their respective industries.

“Knowles designed this new kit to be the simplest and fastest way for product designers to prototype new innovations to address emerging use cases including contextually aware voice, ML listening, and real-time audio processing, that require flexible development tools to accelerate the design process, minimize development costs, and leverage new technological advances,” said Vikram Shrivastava, senior director, IoT Marketing at Knowles. “By selecting Raspberry Pi as the system host, we are opening up the ability to add voice and ML to the largest community of system developers that prefer a Linux or Android environment.”

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“According to Q3 2021 Parks Research survey data, over half of US broadband households find it appealing to use voice control for their connected devices. The demand and the opportunity for voice applications is substantial, but it has often been incredibly complex and expensive for OEMs/ODMs to add the latest voice capabilities to devices. Solutions like those being offered by Knowles that enable voice recognition in a cost-effective, reliable and easy to use manner will strongly resonate with consumers,” said Mark Vena, Senior Director, Smart Home Research Practice for Parks Associates.

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The new kit is built around the Knowles AISonic IA8201 Audio Edge Processor OpenDSP, for ultra-low power and high-performance for a plethora of audio processing needs. The audio edge processor combines two Tensilica-based, audio-centric DSP cores; one for high-power compute and AI/ML applications, and the other for very low-power, always-on processing of sensor inputs. The IA8201 has 1MB of RAM on-chip that allows for high bandwidth processing of advanced, always-on contextually aware ML use-cases and memory to support multiple algorithms for an optimal user experience.

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Using the Knowles open DSP platform, the kit includes a library of on-board audio algorithms and AI/ML libraries. Farfield audio applications can be built using the available ultra-low power voice wake, beamforming, custom keywords, and background noise elimination algorithms from Knowles algorithm partners such as Amazon Alexa, Sensory, Retune, and Alango to open up the design possibilities and ensure the freedom needed to support a wide range of voice and audio customization. The kit also features TensorFlow Lite Micro SDK for fast prototyping and product development for AI/ML applications. The TensorFlow Lite SDK allows for porting models developed in larger cloud Tensor Flow frameworks to an embedded platform at the edge, usually with limited compute and lower power consumption, for example, AI inference engines for anomaly detection in verticals such as industrial and commercial.

With options for either two or three pre-integrated Knowles Everest microphones based on product design needs, the kit includes two microphone array boards to help select the appropriate algorithm configurations for the end application. By offering built-in microphone arrays that support the audio and voice capabilities on the IA8201 DSP, OEMs are provided a high-quality, high-performance all-in-one development solution from a single supplier. Developer support is available through the Knowles Solutions Portal for configuration tools, firmware and algorithms that come standard with the kit, allowing for complete prototyping, design, and debugging.

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