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UrbanSound classification using Convolutional Recurrent Networks in PyTorch
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data Use torchaudio_contrib! and some doc May 5, 2019
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train Use torchaudio_contrib! and some doc May 5, 2019
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README.md Updates along torchaudio_contrib Jun 8, 2019
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crnn_audio_classification_UrbanSound8k.ipynb updated notebook with walkthrough steps May 17, 2019
run.py

README.md

PyTorch Audio Classification: Urban Sounds

Classification of audio with variable length using a CNN + LSTM architecture on the UrbanSound8K dataset.

Example results:

Contents

Dependencies

Features

  • Easily define CRNN in .cfg format
  • Spectrogram computation on GPU
  • Audio data augmentation: Cropping, White Noise, Time Stretching (using phase vocoder on GPU!)

Models

CRNN architecture:

Printing model defined with torchparse:

AudioCRNN(
  (spec): MelspectrogramStretch(num_bands=128, fft_len=2048, norm=spec_whiten, stretch_param=[0.4, 0.4])
  (net): ModuleDict(
    (convs): Sequential(
      (conv2d_0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=[0, 0])
      (batchnorm2d_0): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (elu_0): ELU(alpha=1.0)
      (maxpool2d_0): MaxPool2d(kernel_size=3, stride=3, padding=0, dilation=1, ceil_mode=False)
      (dropout_0): Dropout(p=0.1)
      (conv2d_1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=[0, 0])
      (batchnorm2d_1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (elu_1): ELU(alpha=1.0)
      (maxpool2d_1): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)
      (dropout_1): Dropout(p=0.1)
      (conv2d_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=[0, 0])
      (batchnorm2d_2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (elu_2): ELU(alpha=1.0)
      (maxpool2d_2): MaxPool2d(kernel_size=4, stride=4, padding=0, dilation=1, ceil_mode=False)
      (dropout_2): Dropout(p=0.1)
    )
    (recur): LSTM(128, 64, num_layers=2)
    (dense): Sequential(
      (dropout_3): Dropout(p=0.3)
      (batchnorm1d_0): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (linear_0): Linear(in_features=64, out_features=10, bias=True)
    )
  )
)
Trainable parameters: 139786

Usage

Inference

Run inference on an audio file:

./run.py /path/to/audio/file.wav -r path/to/saved/model.pth 

Training

./run.py train -c config.json --cfg arch.cfg
Augmentation

Dataset transforms:

Compose(
    ProcessChannels(mode=avg)
    AdditiveNoise(prob=0.3, sig=0.001, dist_type=normal)
    RandomCropLength(prob=0.4, sig=0.25, dist_type=half)
    ToTensorAudio()
)

As well as time stretching:

TensorboardX

Evaluation

./run.py eval -r /path/to/saved/model.pth

Then obtain defined metrics:

100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 34/34 [00:03<00:00, 12.68it/s]
{'avg_precision': '0.725', 'avg_recall': '0.719', 'accuracy': '0.804'}
10-Fold Cross Validation
Arch Accuracy AvgPrecision(macro) AvgRecall(macro)
CNN 71.0% 63.4% 63.5%
CRNN 72.3% 64.3% 65.0%
CRNN(Bidirectional, Dropout) 73.5% 65.5% 65.8%
CRNN(Dropout) 73.0% 65.5% 65.7%
CRNN(Bidirectional) 72.8% 64.3% 65.2%

Per fold metrics CRNN(Bidirectional, Dropout):

Fold Accuracy AvgPrecision(macro) AvgRecall(macro)
1 73.1% 65.1% 66.1%
2 80.7% 69.2% 68.9%
3 62.8% 57.3% 57.5%
4 73.6% 65.2% 64.9%
5 78.4% 70.3% 71.5%
6 73.5% 65.5% 65.9%
7 74.6% 67.0% 66.6%
8 66.7% 62.3% 61.7%
9 71.7% 60.7% 62.7%
10 79.9% 72.2% 71.8%

To Do

  • commit jupyter notebook dataset exploration
  • use torchaudio-contrib for STFT transforms
  • CRNN entirely defined in .cfg
  • Some bug in 'infer'
  • Run 10-fold Cross Validation
  • Comment things
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