Bird song recognition

Data download

Analysis and modelling of Polish birds songs. Recordings were downloaded from the - xeno-canto.org which is a website dedicated to sharing bird sounds from all over the world (480k, September 2019).

Data can be dowloaded using this jupyter notebook file.

Bird have high inter-species variance - same bird species singing in different countries might sound completely different. We searched for 33 classes of birds, but we used it only if there was over 50 recording per class, we used it for our training. In total, we provide experiments with two ways:

1) for 19 classes of birds - recorded in Poland, Germany, Slovakia, Czech and Lithuania.

1 . Found 418 files for Parusmajor ( 1 )
2 . Found 59 files for Passerdomesticus ( 3 )
3 . Found 107 files for Luscinialuscinia ( 4 )
4 . Found 111 files for Phoenicurusphoenicurus ( 7 )
5 . Found 446 files for Erithacusrubecula ( 8 )
6 . Found 80 files for Phoenicurusochruros ( 10 )
7 . Found 134 files for Sittaeuropaea ( 16 )
8 . Found 105 files for Alaudaarvensis ( 17 )
9 . Found 216 files for Phylloscopustrochilus ( 19 )
10 . Found 564 files for Turdusphilomelos ( 21 )
11 . Found 314 files for Phylloscopuscollybita ( 22 )
12 . Found 365 files for Fringillacoelebs ( 23 )
13 . Found 65 files for Sturnusvulgaris ( 24 )
14 . Found 329 files for Emberizacitrinella ( 25 )
15 . Found 58 files for Columbapalumbus ( 26 )
16 . Found 204 files for Troglodytestroglodytes ( 27 )
17 . Found 53 files for Cardueliscarduelis ( 30 )
18 . Found 97 files for Chlorischloris ( 31 )
19 . Found 667 files for Turdusmerula ( 33 )

2) for 27 classes - recorded worldwide

Data preprocessing

The data should be prepared. Each song is cut into 5 second recordings and preprocessed into melspectrograms. The purpose is to normalize dataset to have same size along the whole dataset in one run, and to denoise recordings. Morover, the data is filtered with a high-pass filter. Data can be preprocessed using this jupyter notebook file.

Dataset split

This file divides our dataset into train, validation and test set in ratio 8:1:1. We can't use preprogrammed functions to do that, because we have divided each of our files into other smallers (i.e. one sound to six images). Putting images made out of same mp3 file might lead to the data leakage and make our results not trustworthy and biased.

Training

We approached the problem of song classification with Convolutional Neural Networks. We have tested it with:

Xception
MobileNets
EfficientNets
Handcrafted CNN's
Other

Results

In progress

Other

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

Project based on the cookiecutter data science project template. #cookiecutterdatascience

wimlds-trojmiasto/birds