Feature Generators#

This guide describes how to add and configure feature generator components in a machine learning project. Feature generators convert raw sensor data into the input format which a model expects. This document covers the Audio Feature Generator (fe_audio) for microphone-based classification models.

1. Add the Audio Feature Generator Component#

For microphone-based classification models, add the Audio Feature Generator component ( fe_audio ) to convert audio into spectrogram features for inference. Image, IMU, and other non-audio models do not require this component.

Add one fe_audio component instance per audio model. Each instance name must match the corresponding ML Model ( ml_model ) instance name.

In Simplicity Studio#

Perform the following steps in Simplicity Studio.

1. Open the project .slcp file and select the SOFTWARE COMPONENTS tab.

2. Navigate to Silicon Labs AI/ML → Machine Learning → Feature Generator, select Audio Feature Generator ( fe_audio ), and then click Install.

1. When prompted, enter an instance name that matches the corresponding ml_model instance (for example, keyword_spotting).

1. After installation, select the instance under Audio Feature Generator and click Configure to open its settings (see Section 2).

Repeat steps 2 to 4 for each audio model in the project.

When editing the .slcp file directly, the component entries look like this:

component:
  - id: fe_audio
    instance:
      - keyword_spottingcomponent:
  - id: fe_audio
    instance:
      - keyword_spotting

2. Configure Audio Frontend Parameters#

The audio frontend converts microphone samples into the int8 features required by the model. Configure these settings to match the settings used during model training.

In Simplicity Studio#

Perform the following steps in Simplicity Studio.

1. Open the Audio Feature Generator instance in Software Components to review frontend settings. The configuration UI has two groups, shown below for a keyword_spotting instance:

• Configurable parameters: Instance options that you can change using the graphical user interface (GUI), such as Audio Gain. The values you set are written into the generated sl_fe_audio_<instance>_config.h and used during the build process.

• Model defined parameters (read-only, from trained .tflite): Frontend settings from the matching audio model (sample rate, window timing, filterbank settings, and similar). When a value is not embedded in the model, the build uses built-in defaults. Keep the model-provided values unchanged so that inference remains aligned with trained values.

1. Enable parameter-header generation in the matching ml_model instance's .mlconf so that the MVP Compiler exports frontend metadata from the .tflite:

   model: tflite/keyword_spotting.tflite
   codegen:
     model_parameters_header:
       enabled: truemodel: tflite/keyword_spotting.tflite
   codegen:
     model_parameters_header:
       enabled: true

   The fe_audio instance name must match the ml_model instance name (for example, both keyword_spotting). After project generation, the audio frontend uses the model-specific values from <instance>_generated_parameters.h (for example, keyword_spotting_generated_parameters.h).

3. Audio Feature Generation API#

For initialization, feature updates, filling input tensors, and platform-specific behavior, see the ML Audio Feature Generation API reference.

4. Multiple Audio Configurations#

A project with more than one audio model needs a separate fe_audio instance (and matching ml_model instance) for each model.

Declare Instances in the Project#

Add one fe_audio instance per audio model in Simplicity Studio or .slcp (see Section 1). Simplicity Studio generates a matching sl_fe_audio_<instance>_cfg for each instance in autogen/sl_fe_audio_instances.h and autogen/sl_fe_audio_instances.c.

Switch Between Configurations at Runtime#

Only one audio frontend is active at a time. To move from one model's configuration to another:

1. Call sl_ml_audio_feature_generation_deinit() to stop the current frontend.

2. Call sl_ml_audio_feature_generation_init(&sl_fe_audio_<instance>_cfg) with the next instance's config struct.

Example:

sl_ml_audio_feature_generation_init(&sl_fe_audio_first_model_cfg);
/* ... run inference with the first audio model ... */
sl_ml_audio_feature_generation_deinit();
sl_ml_audio_feature_generation_init(&sl_fe_audio_second_model_cfg);
/* ... run inference with the second audio model ... */sl_ml_audio_feature_generation_init(&sl_fe_audio_first_model_cfg);
/* ... run inference with the first audio model ... */
sl_ml_audio_feature_generation_deinit();
sl_ml_audio_feature_generation_init(&sl_fe_audio_second_model_cfg);
/* ... run inference with the second audio model ... */

See the ML Audio Feature Generation API reference for function details.

5. Example Applications#

See the AI/ML Sample Applications documentation for voice-control example projects you can create in Simplicity Studio.