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Discover the challenges and solutions to integrating `CoreML` models with `AudioKit`, preempting audio threading issues while leveraging machine learning for digital signal processing.
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Integrating CoreML Models into AudioKit: A Guide for Custom DSP Effects

In the realm of digital signal processing (DSP), leveraging powerful machine learning models can open up new possibilities for creating innovative audio effects. However, integrating CoreML with AudioKit for this purpose can present its own set of challenges. If you've been curious about how to use CoreML models as custom AudioKit effect nodes, you're not alone.

The Problem

One of the most pressing issues when attempting this integration is ensuring that the predictions made by your CoreML model do not block the audio thread. In audio applications, latency is critical, and any delay can lead to unwanted artifacts, reducing the overall quality of the audio experience. With this in mind, let's explore how to properly handle this integration to maintain seamless audio processing.

Understanding the CoreML Integration

1. Custom AudioKit Node

The first step in utilizing CoreML within your AudioKit application is creating a custom audio node. Here's an overview of how you might approach this:

Load your model(s): Begin by loading your CoreML models within the custom AudioKit node.

Buffering Frames: As audio data flows, buffer the frames until there's enough data to perform a prediction. This is a crucial step because machine learning models typically require multiple inputs to deliver accurate predictions.

2. Using MLMultiArray for Predictions

Once you have buffered your audio frames and accumulated enough data:

Prepare for Prediction: Load the buffered input into an MLMultiArray, which is required to bridge your audio data into the model’s expected format for processing.

Invoke Prediction: Call the model’s prediction method to obtain results.

3. Potential Issues with Blocking

Herein lies a significant challenge. The invocation of the prediction method can block the audio thread, leading to latency and interruptions in audio playback. To alleviate this issue, consider the following approaches:

Solutions for Non-Blocking Audio Processing

A. Multithreading with Grand Central Dispatch (GCD)

Utilizing GCD could be a viable solution to prevent blocking. Here's how you can leverage GCD:

Perform Predictions on a Background Thread: By dispatching the prediction task to a different thread, you can keep your audio processing smooth.

Notify the audio thread once the prediction is complete: Send the results back to the audio processing thread to maintain synchronization without causing latency.

B. Alternatively, Use CPU in the Audio Thread

Some developers have opted not to utilize the Neural Engine for their DSP needs. Instead, they:

Perform Machine Learning directly in the audio thread using the CPU: This approach may differ from best practices of keeping audio processing on dedicated threads, but it can decrease the complexity related to threading and may yield satisfactory performance, depending on the model's computational requirements.

Conclusion

Integrating CoreML models into AudioKit for audio processing can indeed enhance the capabilities of your applications — the potential for implementing advanced DSP effects using machine learning is incredible. However, be sure to manage threading wisely to prevent blocking the audio thread, ensuring a smooth user experience. Whether you choose to dive into GCD, keep it on the CPU, or explore other techniques, the right method depends on your specific audio requirements and performance targets. Happy coding, and may your endeavors lead to innovation in the audio landscape!