Harnessing the Potential of AI at the Edge: Empowering Intelligent Acoustical Evaluation

We have to keep quiet about most of our projects at Product Creation Studio. Occasionally, we get to make a little noise to draw attention to the great development services we offer. But when the objective is intelligent identification of sound sources, we end up making all kinds of noise around the studio…mostly for the purposes of algorithm training and validation.

You may know us as hardware product specialists, but we have a growing team of algorithm and software implementation experts. So when the opportunity arose to implement artificial intelligence (AI) algorithms in a multi-microphone array, our team jumped into action. This opportunity could significantly enhance telephones, smart speakers, voice assistants, conference systems, and home automation devices (e.g., smart locks).

In this article, you’ll get an inside peek into how our team is deploying AI techniques at the edge to deliver real-time acoustical analysis and insights imperceptible to the human ear, such as speech characteristics, voice patterns, traffic noise, and other signals that could revolutionize various industries.

Challenge: Limited Computing Resources in Small Devices?

Deploying AI algorithms on edge devices with limited computing resources presents a significant challenge. Conventional AI models are often computationally demanding, making it impractical to implement them directly on low-power embedded systems or microcontrollers.?

For example, deploying AI algorithms on personal home assistant devices, such as smart speakers or virtual assistants, can be challenging due to their limited computational resources and the need for accurate and timely input and analysis.?

Tasks like real-time language translation in the use of multilingual households or during traveling interactions, as well as complex visual analysis for assistance reading and interpreting documents, labels, or screens, may experience reduced accuracy or slower performance on these devices compared to more robust computing platforms.

However, our team at Product Creation Studio has successfully addressed this challenge by developing efficient model architectures that strike a balance between computational efficiency and accuracy without compromising the battery life of these devices.

Design: Efficient Model Architectures and Sizes for Optimal Acoustic Performance

To optimize the use of limited computing resources in small products or devices, we are currently exploring the size of the model necessary to be the most effective in the test case of noise incidence angle detection.?

Applying this concept to the personal home assistant device, noise incidence angle detection can accurately identify and analyze the direction from which the noise originates. This information can then be used to enhance the device's response, enabling it to prioritize and address relevant sounds more effectively.

For example, the device can identify the direction from which the user’s voice comes while filtering out other background noises, ensuring their commands are heard and understood clearly. By effectively prioritizing and addressing relevant sounds, the personal home assistant device optimizes its performance in real-world environments where there are often competing sounds and distractions.

For efficiency, our team is developing the model using Python and training it in Tensorflow from simulated audio data.? We will also gather additional training data for specific applications related to distinguishing different sounds from one another. By possessing acoustical discrimination capabilities, a device can differentiate between desired sounds and unwanted noise.

Interestingly, the team is finding that more complexity isn’t always better for performance. Currently, our leading choice is a neural network with approximately 10 layers in depth and half a million parameters, which has shown promising results compared to both smaller and larger models.

Test: Edge Computing Paradigms for Rapid and Accurate Acoustical Discrimination

The team will continue making improvements to ensure demonstrations run smoothly on small devices called nVidia Jetson Nano, which are used at the edge of the network. By shifting the processing power from centralized cloud-based systems to the intelligent edge (local), we eliminate the need for constant internet connectivity, which minimizes device latency. This approach ensures faster response times and enables real-time decision-making.

Personal home assistant devices with these capabilities can play a crucial role in providing timely assistance and support. For example, an elderly person with a health condition that requires constant monitoring. The home device can be equipped with sensors to track vital signs, such as heart rate and blood pressure, or even detect falls or abnormal activity patterns.?

With real-time decision-making capabilities and faster response times, the device can analyze the data and identify potential risks and emergencies. It can then take appropriate actions, such as alerting emergency services, caregivers/family members, or even providing assistance through voice guidance.?

This is also particularly beneficial in situations where internet connectivity is temporarily disrupted or slower due to network congestion.?

Edge computing also enhances privacy and security by keeping sensitive audio (and eventually video) data on local devices. This mitigates the risks associated with transmitting data to external servers, providing an additional layer of data protection and privacy.?

Our adoption of the edge computing paradigm enables our clients to have smart and reliable systems with acoustical evaluation capabilities right at their fingertips.

Solution: Other Real-World Applications

Aside from the personal home assistant device, the applications of intelligent room acoustical evaluation powered by our AI algorithms span various industries.?

In the healthcare sector, machine learning audio processing applications like these hold the potential for early detection of respiratory disorders, cardiac abnormalities, and other acoustical-based health conditions. By continuously monitoring and analyzing audio signals, AI algorithms provide valuable insights that can assist healthcare professionals in improving patient care.

In the realm of security systems, AI-powered acoustical analysis can detect and alert authorities to specific events such as glass breaking, aggressive behavior, or other suspicious sounds (like gunshots or vehicle intrusion). This enhances the effectiveness of security measures, enhancing the safety and protection of individuals and properties.

Moreover, intelligent acoustical evaluation has the capacity to augment human communication. By adapting to the unique acoustic conditions of a room, algorithms can isolate different sound sources, amplify or suppress specific others, and optimize sound quality during performances, conferences, lectures, or meetings. This ensures participants enjoy crystal-clear audio experiences, making it easier for everyone to hear and communicate effectively.

AI in Acoustics: Empowering the Human Experience

Through any of our endeavors in AI at Product Creation Studio, we seek to put human experience at the center of the solution. We are excited to drive advancement in products that leverage acoustics for their functionality. Through efficient model architectures, we can overcome the challenges posed by limited computing resources on edge devices. Our approach enables real-time acoustical evaluation while ensuring accuracy and performance.

The possibilities are exciting, ranging from healthcare to the performance stage. As we continue to explore the potential of AI at the edge, we invite you to connect and let us help you apply intelligent data analysis in diverse industries.