urban-sound-classifier¶
Description¶
Note
More information about the service specification can be found in the Core concepts > Service documentation.
This service uses a Convolutional Neural Network (CNN) to classify an urban sound. The follwowing sounds can be detected :
- air_conditioner
- car_horn
- children_playing
- dog_bark
- drilling
- engine_idling
- gun_shot
- jackhammer
- siren
- street_music
The CNN is trained on the MEL-Spectrogram of the sound sample, which transforms the audio into a visual representation of its frequency content over time. This allows the CNN to learn key features from both time and frequency domains, improving its ability to recognize patterns in the audio data.
Note
Urban sound samples can be download from here.
The API documentation is automatically generated by FastAPI using the OpenAPI standard. A user friendly interface provided by Swagger is available under the /docs route, where the endpoints of the service are described.
This simple service only has one route /compute that takes a mp3 file as input, which will be analyzed.
Environment variables¶
Check the Core concepts > Service > Environment variables documentation for more details.
Run the tests with Python¶
Check the Core concepts > Service > Run the tests with Python documentation for more details.
Start the service locally¶
Check the Core concepts > Service > Start the service locally documentation for more details.