Low Complexity Acoustic Scene Classification

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Author: Chukwuebuka Olisaemeka, Anglia Ruskin University Email. Lakshmi Babu Saheer, Anglia Ruskin University Email.

Getting started

1. Clone repository from Github.
2. Install requirements with command: pip install -r requirements.txt.
3. Extract features from the audio files previously downloaded python prepare_data.py.
4. Create a .h5 file with the extracted features.
   • python create_h5.py --dataset_file='/TAUUrbanAcousticScenes_2022_Mobile_DevelopmentSet/meta.csv' --workspace='path'.
5. Run the task specific application with default settings for model quantization python task1.py or ./task1.py

Introduction

This is the codebase for our entry in the Low-Complexity Acoustic Scene Classification in Detection and Classification of Acoustic Scenes and Events 2022 (DCASE2022) challenge. You are permitted to build your own systems by extending this system.

Data preparation

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├── task1_features.yaml   # Parameters for the prepare_data.py file
├── prepare_data.py       # Code to extract features from 1 second files
└── create_h5.py          # Code to create the features_all.h5 file

Network Architecture