https://github.com/imfing/audio-classification

:musical_score: Environmental sound classification using Deep Learning with extracted features
https://github.com/imfing/audio-classification

deep-learning

Last synced: 14 days ago
JSON representation

:musical_score: Environmental sound classification using Deep Learning with extracted features

• Host: GitHub
• URL: https://github.com/imfing/audio-classification
• Owner: imfing
• Created: 2017-12-07T16:46:57.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2020-01-22T05:18:54.000Z (almost 5 years ago)
• Last Synced: 2024-10-12T21:08:13.555Z (29 days ago)
• Topics: deep-learning
• Language: Python
• Homepage:
• Size: 15.7 MB
• Stars: 162
• Watchers: 7
• Forks: 54
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Environmental Sound Classification using Deep Learning

> A project from Digital Signal Processing course

## Dependencies

- Python 3.6

- numpy

- librosa

- pysoundfile

- sounddevice

- matplotlib

- scikit-learn

- tensorflow

- keras

## Dataset

Dataset could be downloaded at [Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/YDEPUT) or [Github](https://github.com/karoldvl/ESC-50).

I'd recommend use ESC-10 for the sake of convenience.

Example:

```

├── 001 - Cat

│  ├── cat_1.ogg

│  ├── cat_2.ogg

│  ├── cat_3.ogg

│  ...

...

└── 002 - Dog

   ├── dog_barking_0.ogg

   ├── dog_barking_1.ogg

   ├── dog_barking_2.ogg

   ...

```

## Feature Extraction

Put audio files (`.wav` untested) under `data` directory and run the following command:

`python feat_extract.py`

Features and labels will be generated and saved in the directory.

## Classify with SVM

Make sure you have `scikit-learn` installed and `feat.npy` and `label.npy` under the same directory. Run `svm.py` and you could see the result.

## Classify with Multilayer Perceptron

Install `tensorflow` and `keras` at first. Run `nn.py` to train and test the network.

## Classify with Convolutional Neural Network

- Run `cnn.py -t` to train and test a CNN. Optionally set how many epochs to train on.

- Predict files by either:

  - Putting target files under `predict/` directory and running `cnn.py -p`

  - Recording on the fly with `cnn.py -P`