How to Create an Audio Model

What Is Machine Learning 2.0

First, what is machine learning? Imagine that your computer can learn new skills by observing and analyzing, such as recognizing animals or understanding simple instructions. This is what we call machine learning.

With machine learning, machines become intelligent. Now, we have integrated this kind of intelligence into mBlock to enable the learning and thinking abilities in your project.

With Machine Learning 2.0, even an AI novice can build an AI model in a few simple steps.

How to Create an Audio Model

In this video, we will create an audio model, including the clap and whistle classes, to show you how to fulfill an audio classification task using Machine Learning 2.0.

First, click the Sprites tab, click extension, and add Machine Learning 2.0.

Then, click Create/Manage model to create a machine learning task.

On the New project page, click New model under Audio Project and name your model.

On the page for model training, you can add data on the left panel for your classification task.

First, in the Background Noise class, click Mic and record 20 seconds of the background noise. You can record multiple times, just make sure that the audio duration is 20 seconds or longer.

Then, click Extract Samples to extract samples that you need.

Name the second and third classes "Clap" and "Whistle" respectively, and record sounds of 8 seconds or longer for each class.

After collecting the data, click Train Model in the Training area. mBlock automatically learns how to identify clap and whistles based on the audio data. To modify the training parameters, click Advanced.

After completing model training, you can preview the classification results on the right panel. The probability that an object recognized belongs to each class is displayed under the video panel.

You can modify the dataset or parameters anytime to retrain the model. If you are satisfied with the classification result, click Use model in the upper-right corner to integrate the model into blocks. Now you can apply these AI-based blocks to your mBlock projects.

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