This repo contains code for my thesis: Ship-radiated noise recognition using spectrogram.

• A mean average precision (mAP) of 71.6% is achieved using our proposed Gf+ system, outperforming the Cnn10 baseline of 64.1%.
• A total number of parameters in a PyTorch model is only increased by 1.06M.
• Allows on-the-fly spectrogram extraction and fine-tuning.

Environments

The codebase is developed with Python 3.7 cuda 10.1

Gf+ model

The visualization of general framework looks like:

Train models from scratch

1. Download dataset

Please see data/readme.md for details of Email.

2. Pack waveforms into hdf5 files

The utils/prepdata.py script is used for packing all raw waveforms into hdf5 files for speed up training and divide them into 4 folds. The data files looks like:

data
└── DeepShip
     ├── class1
     |    ├── d1
     |    ├── ...
     |    └── dx
     |        ├── 0001.wav
     |        ...
     |        └── 000x.wav
     └── classx

The utils/prepdemon.py script is used for generating the demon spectrogram, everything else is the same.

3. Train

The train.sh script contains training, saving checkpoints, and evaluation.

python pytorch/main.py train \
    --workspace=$WORKSPACE \
    --hdf5_path=$DATA_DIR \
    --holdout_fold=4 \
    --model_type='Cnn10_gf' \
    --loss_type=clip_bce \
    --augmentation='none' \
    --learning_rate=1e-4 \
    --batch_size=16 \
    --stop_iteration=2000 \
    --feature_name='mel' \
    --learnable='none'

Results

The CNN models are trained on a single card V100-SXM2-32GB. The training takes around 3 - 5 hours.

Wandb have been used for experiments recording. Please check https://wandb.ai/pretty1sky/Features_try?workspace=user-pretty1sky.

The variation of the mAP of CNN10 and Gf with the number of iterations under four different features before and after fine-tuning learnable parameter (left) and the Grad-CAM of CNN block (right) are shown below.

Demos

We apply the audio tagging system to build a sound event detection (SED) system.

The demo code is available at https://github.com/yinkalario/General-Purpose-Sound-Recognition-Demo.

Acknowledgement

We appreciate the open source of the following projects:
Gabor Convolutional Networks   PANNs   nnAudio

We appreciate the open source of the following datasets:
DeepShip   ShipsEar   AudioSet