OpenSoundscapeΒΆ
OpenSoundscape is free and open source software for the analysis of bioacoustic recordings (GitHub). Its main goals are to allow users to train their own custom species classification models using a variety of frameworks (including convolutional neural networks) and to use trained models to predict whether species are present in field recordings. OpSo can be installed and run on a single computer or in a cluster or cloud environment.
OpenSoundcape is developed and maintained by the Kitzes Lab at the University of Pittsburgh.
The Installation section below provides guidance on installing OpSo. The Tutorials pages below are written as Jupyter Notebooks that can also be downloaded from the project repository on GitHub.
- Audio and spectrograms
- Manipulating audio annotations
- Download example files
- View a subset of annotations
- Saving annotations to Raven-compatible file
- 1. Split Audio object, then split annotations to match
- 2. Split annotations directly using splitting parameters
- 3. Split annotations using your own clip DF
- Match up audio files and Raven annotations
- Split and save the audio and annotations
- Sanity check: look at spectrograms of clips labeled 0 and 1
- Prediction with pre-trained CNNs
- Beginner friendly training and prediction with CNNs
- Preprocessing audio samples with OpenSoundscape
- Modifying the preprocessor of the CNN class
- Download labeled audio files
- Load dataframe of files and labels
- Initialize preprocessor
- Generate a sample from a Dataset
- Subset samples from a Dataset
- About Pipelines
- About actions
- View default parameters for an Action
- Modify Action parameters
- Bypass actions
- Example: return Spectrogram instead of Tensor
- analyse the output at steps of interest
- adding the preprocessor to a CNN
- Advanced CNN training
- RIBBIT Pulse Rate model demonstration
- Annotations
- Audio
- AudioMoth
- Audio Tools
- Spectrogram
- CNN
- torch.models.utils
- CNN Architectures
- torch.architectures.utils
- WandB (Weights and Biases)
- Data Selection
- Datasets
- GradCam
- Loss
- Safe Dataset
- Sampling
- Metrics
- Image Augmentation
- Actions
- Preprocessors
- preprocessors.utils
- Tensor Augment
- RIBBIT
- Signal Processing
- Taxa
- Localization
- helpers