Source code for training, decoding and evaluating an automatic monkey call recognizer.

• Free software: GPL3 license

• Classification and decoding of calls in isolation
• Automatic transcription of audio files

Install python 2.7.x using your system's package manager.

Optionally, install virtualenv to keep separate the required packages and then do:

$ virtualenv -p $(which python2.7) ~/mcr_venv && source ~/mcr_venv/bin/activate

Next, install the required packages:

$ pip install -r requirements.txt

And you're good to go.

The system consists of two sets of scripts with similar interfaces, for segmented recognition and transcription respectively. Both sets can be found in the src folder. We will discuss these scripts below. First, we need to discuss the annotation and configuration formats and

The train, predict and eval scripts all require an annotation file. The annotation file is a csv-file with filename, start and end columns and an optional label column which is needed only for the train and eval scripts. The rows in the annotation file point to segments of audio files, denoted by a filename, start and end times and possible call label. As an example, suppose we have two one-second audio recordings, /home/me/A.wav and /home/me/B.wav and we have annotated a PYOW call in file A at the time interval 0.100 - 0.200 and a HACK call in file B at the time interval 0.500 - 0.600; we then make an annotation file (annotation.csv) with the following contents to train our classifier::

filename,start,end,label
/home/me/A.wav,0.000,0.100,SIL
/home/me/A.wav,0.100,0.200,PYOW
/home/me/A.wav,0.200,1.000,SIL
/home/me/B.wav,0.000,0.500,SIL
/home/me/B.wav,0.500,0.600,HACK
/home/me/B.wav,0.600,1.000,SIL

where we used SIL to denote the absence of calls. Note that, although we have two audio files, we only make a single annotation file for the classifier script. In case we have a trained classifier available and wish to use it to transcribe a new one-second recording C.wav we have made and haven't manually annotated, we can make an annotation file without a label column::

filename,start,end
/home/me/C.wav,0.000,1.000

The train scripts require a configuration file which lists the settings for the feature extractors and the hyperparameters for the classifiers. This file needs to be in toml format. Examples of these files with default settings can be found in src/segmented.cfg and src/transcription.cfg. These example files list all the parameters to the system, so if you wish to play around with different settings, simply changing these configuration files is a good option.

Segmented classification refers to classifying individual monkey calls in isolation, i.e. we know their exact location in an audio stream and need to learn their identity. The following scripts are supplied:

• src/segmented_train.py
• src/segmented_predict.py
• src/segmented_eval.py

For example, to train a classifier using the above annotated calls, we do::

$ python segmented_train.py annotation.csv segmented.cfg trained.clf

Note that this will only consider the annotated calls, the script will ignore any non-call (SIL) segments in the audio files.

To use this trained classifier to predict the annotations again, we do::

$ python segmented_predict.py annotation.csv trained.clf predicted.csv

To compare the predicted labels to the annotation, we do::

$ python segmented_eval.py annotation.csv predicted.csv

Which will print scores to the standard output.

For more information about how to use the scripts, see their help messages.

Transcription refers to the automatically transcribing entire audio files, marking where calls occur and their identity and where there is only silence (or, more likely, background noise). The following scripts implement a complete system for training, decoding and evaluating a transcription classifier:

• src/transcription_train.py
• src/transcription_predict.py
• src/transcription_eval.py

The scripts for complete transcription of audio files follow the same conventions as those above. They will however take considerably longer to run.