This project uses python3 for most code, with the exception of python2 for Mel spectrum feature extraction. The following python3 libraries are required:
- numpy
- scipy
- scikit-learn
- matplotlib
- librosa
Most of these should be available using pip install.
For Mel spectrum feature extraction, the following python2 library is required:
- pyAudioAnalysis
The webscraper is a standalone tool which can be used to create datasets from https://xeno-canto.org.
./scraper/scraper.py <query> will query the website for a species of bird and gather the metadata for the resulting recordings into the metadata.csv file.
./scraper/download.py will attempt to download the audio recording associated with each entry in the metadata and place them in the ./scraper/songs/ directory.
./preprocess.py performs feature extraction on the metadata.csv present in the same directory. There should also be a ./songs/ directory in the same location with recordings corresponding to each item in the metadata. The extracted features are pickled and placed in ./features
./pyAudioPreprocess extracts Mel spectrum features us pyAudioAnalysis, and pickles them to F.
./train.py [name] trains the model on 5 different algorithms and tests them using 5-fold cross-validation. There must be files named [name]_features and [name]_metadata in the same directory for this to work. If no name is provided, it defaults to using metadata.csv and features. Currently it always defaults to F for the mel spectrum features.
A few datasets are provided for classification:
bbs: The British Bird Song dataset found at https://www.kaggle.com/rtatman/british-birdsong-datasetdataset1: 6 species of birds, 512 samples, from https://xeno-canto.orgdataset2: A slightly trimmed down version of dataset1; 306 samples
The current clustering method does not tend to provide particularly interesting results, so this section can be mostly ignored.
./cluster.py [name] performs hierarchical clustering on each audio recording sample in the dataset and produces a dendrogram. The dendrogram tends to be quite difficult to read for large datasets, so sampling the population is advised.
./similarity.py [name] performs hierarchical clustering on the bird species.