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Version: v0.3.0

Tutorial 1C: Annotation task: Audio classification

Overview​

This tutorial describes the process of annotating and specifying an annotation task rubric for an audio classification annotation task. To highlight the process, we are going to annotate a dataset that contains 5-second-long recordings of environmental sounds organized into ten classes (with 40 examples per class). Clips in this dataset have been manually extracted from public field recordings gathered by the Freesound.org project.

This tutorial also explores how you can download the fully annotated dataset supported in H2O Hydrogen Torch.

Step 1: Explore dataset​

This tutorial uses the preloaded esc10-audio-demo demo dataset, which contains 400 audios, each depicting the sound of a chainsaw, dog, helicopter, rain, rooster, etc. Let's quickly explore the dataset.

  1. On the H2O Label Genie navigation menu, click Datasets.
  2. In the datasets table, click esc10-audio-demo.

Dataset details esc10-audio-demo recordings of 5 seconds each

Step 2: Create an annotation task​

Now that we have seen the dataset let's create an annotation task that enables you to annotate the dataset. An annotation task refers to the process of labeling data. For this tutorial, an audio classification annotation task refers to assigning a categorical target label to an audio clip. Let's create an annotation task.

  1. Click New annotation task.
  2. In the Task name box, enter tutorial-1c.
  3. In the Task description box, enter Annotate a dataset containing samples of environmental sounds.
  4. In the Select task list, select Classification.
  5. Click Create task.

Step 3: Specify annotation task rubric​

Before annotating our dataset, we need to specify an annotation task rubric. An annotation task rubric refers to the labels (for example, object classes) you want to use when annotating your dataset. For our dataset, the following are the multiple categorical target labels we want to specify:

  • chainsaw
  • clock_tick
  • crackling_fire
  • crying_baby
  • dog
  • helicopter
  • rain
  • rooster
  • sea_waves
  • sneezing

Let's define the annotation task rubric.

  1. In the New class name box, enter chainsaw.
  2. Click Add.
  3. Click Add class.
  4. In the New class name box, enter clock_tick.
  5. Click Add.
  6. Repeat the above steps until you create all labels.
  7. Click Continue to annotate.

audio-classification-add-classes.png

note

H2O Label Genie supports multi-label audio classification annotation tasks.

Step 4: Annotate dataset​

Now that we have specified the annotation task rubric, let's annotate the dataset. In the Annotate tab, you can individually annotate each audio clip in the dataset. Let's annotate the first audio.

  1. Select the label that you associate with the sound audio you hear (for example, clock_tick).
  2. Click Save and next.
    Note
    • Save and next saves the annotated audio
    • To skip an audio clip to annotate later: Click Skip.
      • Skipped audio clips (samples) reappear after all non-skipped audios are annotated
    • To download all annotated samples so far, consider the following instructions:
      1. Click the Export tab.
      2. In the Export approved samples list, select Download ZIP.
        note

        H2O Label Genie downloads a zip file containing the annotated dataset in a format that is supported in H2O Hydrogen Torch. To learn more, see Downloaded dataset formats: Audio classification.

Download annotated dataset​

After annotating all the audio clips, you can download the dataset in a format that H2O Hydrogen Torch supports. Let's download the annotated dataset.

  1. In the Annotate tab, click Download approved samples. Notification of completed annotation task ready to export approved samples
  2. In the Export approved samples list, select Download ZIP.

Summary​

In this tutorial, we learned the process of annotating and specifying an annotation task rubric for an audio classification annotation task. We also learned how to download a fully annotated dataset supported in H2O Hydrogen Torch.

Next​

To learn the process of annotating and specifying an annotation task rubric for other various annotation tasks in computer vision (CV), natural language processing (NLP), and audio, see Tutorials.

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