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Acoustic detection for warning of drowning accidents

This project is a semester project for the Computer Engineering (AI, Vison and Sound) Master's program at Aalborg University, Denmark. © 2023

Setup

  • Clone the repossitory
  • Install requirements
  • Move measurement files into designated folder structure as shown below
Audio File Label File
data/measurment-2/raw/230428-006.wav data/measurment-2/labels/230428-006.txt
data/measurment-2/raw/230428-003.wav data/measurment-2/labels/230428-003.txt
data/measurment-1/amplified_jumps/230320-008-jump-1.wav data/measurment-1/labels/230320-008-jump-1.txt
data/measurment-1/amplified_jumps/230320-008-jump-2.wav data/measurment-1/labels/230320-008-jump-2.txt
data/measurment-1/amplified_jumps/230320-008-jump-3.wav data/measurment-1/labels/230320-008-jump-3.txt
data/measurment-1/amplified_jumps/230320-009-jump-1.wav data/measurment-1/labels/230320-009-jump-1.txt
data/measurment-1/amplified_jumps/230320-009-jump-2.wav data/measurment-1/labels/230320-009-jump-2.txt
  • Run cnn_make-clips.py
  • Run train_ccn.py (train_cnn.py will create numpy files that train_svm.py and train_lda.py depends on)
  • If you wish to run baseline_model, then also run the make-clips.py file first.