v0.3.0

Full Changelog: 0.2.1...0.3.0

This new version is more general, not only dedicated to xeno-canto.
Soundscapes could also be processed with bambird.

In this new release:

• Change in clustering function
  change PCA dimension reduction into UMAP
  fix a bug in determining the biggest cluster

• Change in configuration function
  yaml function is now hidden in the call of load_config function.
  fix a bug: the default RANDOM_SEED is now loaded if no configuration file is found

• Change in features and segmentation functions
  the SNR associated with each region of interest is now provided.

• Change in the default_config.yaml configuration file

• Change in Setup.py
  Requirement scikit-image>= 0.19.2 and umap-learn

v0.2.1

Fix a bug in the config_default.yaml configuration file

v0.2.0

The release v0.2.0 is the last stable release.

The functions available in the package are:
from config.py

• load_config : Load the configuration file to set all the parameters of bambird

from dataset.py

• query_xc : Query metadata from Xeno-Canto website with audiofile depending on the search terms. The audio recordings metadata are grouped and stored in a dataframe.
• download_xc: Download the audio files from Xeno-Canto based on the input dataframe. It will create directories for each species if needed
• grab_audio_to_df: create a dataframe with all recordings in the directory. The first column name corresponds to full path to the filename. The second column name correspond to the filename alone without the extension
• change_path: change the path to the audio in the dataframe. This is usefull when the audio are moved from their original place

from segmentation.py

• extract_rois_core: function called by single_file_extract_rois. Define a specific process to extract Rois. In this case, the function extract the most energetic part of songs/calls.
• extract_rois_full_sig:f unction called by single_file_extract_rois. Define a specific process to extract Rois. In this case, the function extract the full songs/calls.
• single_file_extract_rois: Extract all Rois in a single audio file
• multicpu_extract_rois: Extract all Rois in the dataset (multiple audio files)

from features.py

• compute_features: Compute features of a single Roi such as shape (wavelets), centroid and bandwidth
• multicpu_compute_features: Compute features such as shape (wavelets), centroid and bandwidth of all Rois in the dataset (multiple audio files)

from cluster.py

• find_cluster: Clustering of ROIs. Use DBSCAN or HDSCAN clustering method for several reasons :
  * DBSCAN does not need the number of clusters to do the clustering
  * DBSCAN is able to deal with noise and keep them outside any clusters.
  So, the goal of the clustering is to aggregate similar ROIs
  which might correspond to the main call or song of a species. If several
  clusters are found, which means that we might have ROIs corresponding to
  different calls and/or songs for the species, we can keep the cluster with
  the highest number of ROIs or all the clusters.
• cluster_eval: Evaluation of the clustering (requires annotations or any other files to compare with the result of the clustering)
• overlay_rois: Overlay Rois with colors and number depending on the cluster number or the label.
• mark_rois: Add a marker to the audio filenames of each Roi depending on the result of the evaluation of the clustering (TN, FN, TP, FP)
• unmark_rois: Remove the markers