# Audio classification Source: [https://docs.qualcomm.com/doc/80-70022-50/topic/audio-classification.html](https://docs.qualcomm.com/doc/80-70022-50/topic/audio-classification.html) The **gst-ai-audio-classification** application shows audio classification using input from either a file source or a microphone. It displays both the classification results and a video preview. The following figure shows the pipeline, which gets the input from a file or a microphone, preprocesses it, and runs inferences on AI hardware. The results are displayed on the screen. For information about the plugins used in the pipeline flow, see Pipeline flow. Figure : gst-ai-audio-classification pipeline Qualcomm Open source Waylandsink filesrc qtdemux h264parse V4l2h264dec mpg123audioparse/flacparse mpg123audiodec/flacdec audioconvert audioresample audiobuffersplit qtimlaconverter qtimflite pulsesrc audiobuffersplit qtimlaconverter qtimflite qtimlpostprocess qtimlpostprocess qtivcomposer ## Sample model and label files Table : Sample model and label files for gst-ai-audio-classification | Runtime | Model files | Label files | | :--- | :--- | :--- | | LiteRT | yamnet.tflite | yamnet.json | ## Run the application on the target device The sample application uses the /etc/configs/config-audio-classification.json file to read the input parameters. To create your own config JSON file, use [config-audio-classification.json](https://git.codelinaro.org/clo/le/platform/vendor/qcom-opensource/gst-plugins-qti-oss/-/blob/imsdk.lnx.2.0.0.r2-rel/gst-sample-apps/gst-ai-audio-classification/config-audio-classification.json?ref_type=heads) as a reference. 1. Ensure that you complete the Prerequisites. 2. Update the config JSON file based on the model, input stream, and other properties. For more information, see Config JSON field description. 3. Use the following format of the config-audio-classification.json file. { "file-path": "", "model": "", "labels": "", "threshold": , "runtime": "", "codec": "" }Copy to clipboard For example, run the application using input from a file, MP3 encoding, and GPU runtime: { "file-path": "/etc/media/video-mp3.mp4", "model": "/etc/models/yamnet.tflite", "labels": "/etc/labels/yamnet.json", "runtime": "cpu", "threshold": 20, "codec": "mp3" }Copy to clipboard 4. Run the gst-ai-audio-classification application: gst-ai-audio-classification --config-file=/etc/configs/config-audio-classification.jsonCopy to clipboard 5. To display the available help options, run the following command in the SSH shell: gst-ai-audio-classification -hCopy to clipboard 6. To stop the use case, use CTRL + C. ## Expected output The output video and classified audio are played on the screen. ## Pipeline flow The following table lists the plugins used in the audio classification pipeline:| Plugin | Description | | --- | --- | | File source: filesrc |

  • Captures the video stream using filesrc, followed by
    qtdemux, which demultiplexes the stream.


  • Uses tee to split the stream for inferencing.


| | h264parse | Parses the H.264 video. | | v4l2h264dec | Decodes the video. | | mpegaudioparse or flacparse | Parses the audio (MP3 or FLAC). | | mpg123audiodec or flacdec | Decodes the audio (MP3 or FLAC). | | audioconvert | Converts the audio buffers between various possible
formats. | | audioresample | Resamples the audio buffers to different sample rates. | | pulsesrc | Reads the audio from the microphone. | | audiobuffersplit | Splits the incoming audio buffers into equal sized
chunks. | | qtimlaconverter | Performs preprocessing on the audio stream and converts the
stream to a tensor stream.
The audio classification model uses
this tensor stream for inferencing. | | qtimltflite | Performs inferencing using the YAMNet model. | | qtimlpostprocess | Uses yamnet module to handle the audio classification inference
results:

  1. Applies a threshold to the chosen number of results.


  2. Creates text overlay for classes.


| | qtivcomposer | Combines the text overlay for classification results and video
preview. | | Waylandsink |

  1. Waylandsink submits the video stream received on its sink
    pad to Weston.


  2. Weston renders the video stream on a local display.


| ## Config JSON field description The different parameters available to configure the JSON file and run the use case are as follows: | Field | Values/description | | :--- | :--- | | **runtime** | Use one of the following runtimes:

  • cpu


  • gpu


| | **Input source** | Use one of the following input sources:

  • file-path: The directory path to the video
    file.


  • Microphone


| | **threshold=<integer>** | Use any integer between 1 and 100. | | **codec** | The audio codec of input video:

  • MP3 (default)


  • FLAC


| ## Related information Audio classification decode and display with LiteRT **Parent Topic:** Run AI/ML sample applications Last Published: Feb 20, 2026 Previous Topic Face recognition Next Topic Metadata parsing