Revolutionizing Sound Analysis: Unveiling Cloud-Based Sound ID

We are excited to announce the launch of our new Cloud-based SoundID model, which is capable of detecting over 400 different sound classes with exceptional accuracy. This powerful deep learned technology has enormous potential in a range of industries, including healthcare, security, automotive, and industrial automation.

The Cloud-based SoundID model is highly accurate and can provide a list of the most likely sounds in a given audio stream. This capability is especially valuable in applications where real-time analysis is needed and the sound environment is monitored. The model can run in streaming mode, giving accurate, frequently updated, estimates of the current sounds in the audio stream or a low power energy detector can be used on device to trigger short period spot analyses. The system can then identify the most relevant sounds and respond accordingly in either an always on or a lower power lower cost mode.

The model uses deep learning algorithms to build highly accurate and efficient models, capable of detecting even subtle variations in sound. By training the model on a vast dataset, we have been able to achieve remarkable accuracy while maintaining low latency and high performance. This makes the Cloud-based SoundID model an ideal solution for applications where a range of sounds can occur and accuracy as well as a real-time response are critical.

The Cloud-based SoundID model is also highly customizable, making it suitable for a wide range of applications. Our team of experts can work with clients to identify their specific needs and develop customized solutions tailored to their requirements. We use cutting-edge machine learning frameworks like Torch and TensorFlow to implement the models efficiently and optimize them for different platforms.

Sensory on device low power sound ID can also be combined with the cloud approach, either for cloud-based verification of sounds or as a backup for unknown sounds, thus keeping system power and cloud usage to a minimum. The on-device sound ID already supports 16 sound types can run at an OS or even on select microprocessors and DSPs for ultra low power sound identification. With this approach, we could add specific sounds that the product or application requires thus providing an on device, low power always on solution with the new SensoryCloud approach.

In conclusion, the Cloud-based SoundID model offers a range of exciting opportunities for a variety of industries. Its ability to detect over 400 different sound classes with exceptional accuracy makes it a powerful tool for applications that require a broad sound analysis capability. The model’s customizability also makes it suitable for a wide range of applications, from analyzing part failures to confirming performance. We look forward to seeing this technology’s impact and the many possibilities it will unlock for our clients. Contact our team to learn more!