Audio Identification Solutions

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解决方案
文件
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eDesignSuite

Using Edge Impulse, it is possible to create intelligent device solutions embedding tiny Machine Learning and DNN models. The Cloud-based solution abstracts the complexity of real-world sensor data collection and storage, data features extraction, ML and DNN models training and conversion to embedded code, and model deployment on STM32 MCU devices. Without local AI framework installation, engineers can generate and export the model into their STM32 projects with a single function call. All generated Neural Networks now fully utilize STM32Cube.AI to ensure that they run as fast and energy efficiently as possible, and firmware can be fully customized using STM32CubeMX.

Deploying machine learning (ML) models on microcontrollers is one of the most exciting developments of the past years, allowing small battery-powered devices to detect complex motion, recognize sounds, classify images or find anomalies in sensor data. To make building and deploying these models accessible to every embedded developer STMicroelectronics and Edge Impulse have been working together to integrate support for STM32CubeMX and STM32Cube.AI to Edge Impulse. Edge Impulse Cloud is now capable of exporting Neural Networks through a local STM32Cube.AI engine to ensure the best possible efficiency into a CMSIS PACK compatible with STM32CubeMX projects. This gives developers an easy way to collect data, build models, and deploy to any STM32 MCU.