Similar to visual data, collecting and curating sound data that accurately reflects real-world scenarios is a major hurdle in training effective machine learning models. The time, effort, and costs involved can be substantial, posing significant barriers, especially for projects with limited resources. As our mission is to lower the barriers of creating and utilizing ML and AI tools, we aim to drastically reduce these types of impediments, making the process faster, cheaper, and as reliable as possible.
Edge AI enables smart devices to perform machine learning tasks right at the source of data collection, using small models that are optimized for performing scoped tasks as long as they are trained on quality data. That’s why we emphasize that quality is key: "Garbage in, garbage out."
Improving sound datasets with generative AI
To enhance your sound datasets, we've been working on integrating Edge Impulse with ElevenLabs.io. Edge Impulse excels in crafting and optimizing models for edge computing. Meanwhile, ElevenLabs.io offers advanced capabilities for creating highly realistic sound effects. This integration enables you to expand your datasets with sounds that are difficult or expensive to record naturally, using state-of-the-art generative techniques provided by ElevenLabs.io. This integration not only saves time and money but also boosts the accuracy and reliability of the models we deploy on edge devices.
It is accessible now in the new Synthetic data tab inside the Data Acquisition segment of Edge Impulse for Enterprise users. Sign up for a free Enterprise Trial to test it out.
Case Study: Detecting the Sound of Breaking Glass
In the attached video, we focus on a practical application: Train a model to detect the sound of glass breaking. This simple walk-through shows:
- How to generate an audio dataset using a text-to-sound generative AI models directly in Edge Impulse
- An example of a trained model
- A demonstration of the model inference
This use case could be applied in smart security systems or for safety enhancements in industrial environments.
If you have ever faced the challenge of collecting high-quality sound data that is prohibitively expensive or scarce, we hope this integration will make your lives easier!