Teach Blocks to recognise sounds, then use the predictions in a project with Marty the Robot.

===== Before you start =====

Open [[https://robotical.app/blocks|Blocks]] in a supported browser. To make Marty respond, select Marty in the Devices panel and connect over Bluetooth. You can collect examples and train a model before connecting a robot.

The model uses your computer or tablet's microphone. Allow microphone access when prompted. Select the target you want to use before creating the model: saved models belong to that target.

===== Create and name an audio model =====

Open the **Machine Learning** tab, select **Add New Model** (the + button at the bottom left), then choose **New Audio Model**. If an unsaved-changes warning appears, continue only after saving any model you want to keep.

Enter a clear name in **Model name**, such as “Audio example”. The editor guides you through **Collect examples**, **Train**, and **Test & save**.

{{ :martyv2:userguides:martyblocks:machinelearning:audio-setup-2026-10.jpg?nolink&1000 |Audio model editor with class controls and the collection, training and testing workflow}}

===== Add classes and record examples =====

A class is a sound you want the model to recognise, such as a clap or a whistle. Type its name in **Add class**, then select **+ Add class**. Use descriptive names in your own project; “Class 1” in the screenshots is an example name.

Select a class, then press **Record examples for [class name]**, or **Record Data** on its card. Each recording captures a short sound sample and stops automatically. Repeat to collect several examples of that sound. You can play back or delete individual examples before saving the model.

The editor creates a **background noise** class, displayed as `_background_noise_`. Record at least **10 examples** for this class with your usual room noise and without deliberately making the sounds you want to recognise. This class cannot be renamed or removed.

You need at least two classes, including background noise, and examples in every class before training. Aim for at least 10 varied examples per sound class too. Record the same sound at different distances and volumes, and keep the number of examples reasonably balanced.

===== Train the model =====

When the editor says **Ready to train**, select **Train model**. Wait for training to finish. The **Training loss** graph shows how the model fits the training examples; a falling line alone does not prove that it will recognise new sounds correctly.

{{ :martyv2:userguides:martyblocks:machinelearning:audio-training-2026-10.jpg?nolink&700 |Audio model training loss and ready-to-test state}}

===== Test with new sounds =====

Select **Test model**. Make each sound again and watch **Live predictions**, which shows the best match and the confidence for every class. Also try ordinary background noise. Test with sounds you did not use during collection.

{{ :martyv2:userguides:martyblocks:machinelearning:audio-predictions-2026-10.jpg?nolink&700 |Live audio predictions and confidence bars}}

Select **Stop testing** when finished. If the predictions are unreliable, add or replace examples and train again before saving. Use **Test again** to repeat the test.

===== Save the model and back up the project =====

Select **Save model** when you are happy with the results. The model appears in the Models list and becomes available in Code.

{{ :martyv2:userguides:martyblocks:machinelearning:audio-save-2026-10.jpg?nolink&1000 |Test again and Save model controls}}

**A saved model cannot be given more training examples or retrained.** Create a new model if you need to improve it later.

To keep a portable backup of your code and saved model, open **Save/Load** and select **Save to File** under **Portable File Storage**. Keep the downloaded `.sb3` file. Use **Choose File** in the same section to reopen it later. Saving the model and saving the whole project are separate steps.

===== Load the model in Code =====

Select **Code**, then the **Machine Learning** blocks category. Add **Load Machine Learning model [Audio example]** to your program and choose your saved audio model from its menu. If the model is missing, select the target on which you created it and check that you saved it.

Run this block to load the model. Sound classification starts automatically, using your device's microphone. Use **turn classification [off]** to pause predictions, and **turn classification [on]** to resume them.

{{ :martyv2:userguides:martyblocks:machinelearning:audio-code-2026-10.jpg?nolink&360 |Blocks for loading an audio model, responding to sound labels and adjusting classification}}

===== Use predictions in your program =====

The screenshot shows separate example blocks. To try the response without moving Marty, create a variable named **prediction** using **Variables → Make a Variable**. The example event and condition set this variable to “Class 1”.

After loading the model, your sound class names are available in the sound-label menus. The background-noise class is excluded from those menus.

  * **Event:** put your response beneath **when received sound label: [class]**.
  * **Condition:** use **sound [class] detected** in an **if** block, inside a loop if you want to check repeatedly.
  * **Reporter:** use **sound label** for the recognised class name, or **confidence of sound [class]** for its confidence. You can tick the checkbox beside **sound label** in the Machine Learning category to display it on the stage.

Choose your own class in place of “Class 1” in the example. Begin with a response that does not move the robot, then add a Marty action once you have checked the predictions. Put Marty on a clear, stable surface before running movement commands.

===== Adjust the classification interval and confidence =====

**Label once every [1] seconds** changes how often the model checks for a sound. The default interval is one second.

**set confidence threshold [0.5]** changes the minimum confidence needed for a recognised label. The default is 0.5 (50%). A higher threshold can reduce unwanted responses but may miss some sounds. If no prediction meets the threshold, **sound label** can be empty.

Use **turn classification [off]** when your program no longer needs predictions. Save the project again after changing your code.