This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep – 2023), Automated cataloging of oyster toadfish (Opsanus tau) calls using template matching and machine learning. The software accompanying this paper is known as ToadFishFinder, and the classifier model presented in the paper is v4. It consists of more than 10000 labeled toadfish and 10000 labeled other signals. Labeled spectrogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 1350 ms long. The center 850 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1)  MATLAB (.mat) files containing the 'boatwhistle' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the ToadFishFinder model or develop alternative classifiers. 

Labeled signals were extracted from passive acoustic data collected at eight sites within southwestern Pamlico Sound near its confluence with the Pamlico and Neuse River estuaries. As part of a larger effort to monitor the evolution of these reef habitats, each site was outfitted with a SoundTrap 300 hydrophone affixed ~0.5 m above the seabed a the top of a metal stake anchored with a concrete block. Monitoring extended from the fall of 2016 through the fall of 2017, and from the Spring of 2018 through the Fall of 2018. Over most of the monitoring period, the recorders were programmed to capture a 2-minute-duration recording every 20 minutes. Acoustic data were collected at a rate of 96,000 samples/second. They were subsequently resampled to a rate of 24 kHz with the application of an anti-aliasing filter.

To read the pre-made spectrogram images 

For example:

Unzip the spectrograms_TFv4.zip file 

Images are stored in 'bwhistle' and 'other' folders.   They can be read into MATLAB as: 

         >>  im=imread('t_DB_335589429.180508212002_078.7066250.jpg');

         >>  figure; imshow(im)

To read the waveform snippets into MATLAB: 

Waveform data snippets are also provided for these labeled signals. Each snippet is 1350 ms long, with the training and test data (used in generating the spectrograms) representing the center 850 ms of data. These waveforms are unfiltered, pressure corrected and sampled at 24 kHz.  

For example: 

       >> load toadfish_wavefrom_database_TFv4.mat

       >> whos

        fs                     1x1                         8  double                            % sample rate   = 24,000 Hz 

        bwhistlecalls       2161x6009             103883592  double         % each column is a call example 

        station             6009x1                   649846  cell                        % name of stations 

        t                       2161x1                   17288  double                    % time vector for plotting 

        utc                   6009x1                   96144  datetime                 % approximate UTC time 

Station names and locations are given below:  

            >> figure; plot(t,boatwhistlecalls(:,1);  % show first waveform 

            >> figure; plot(t(6000:26401), bwhistlecalls(6000:26401,1)); % show portion used in spectrogram generation

To recreate the spectrogram, use the function provided as part of ToadFishFinder

                >> tfMakespectro(bwhistlecalls(6000:26401,1)); 

loading other_waveform_database_TFv4.mat will similarly load the other signal class. 

To access the waveform snippets in wavfile format: 

The snippets are also saved as individual (32-bit) wavfiles.  Each file contains 1350 ms of data, with the training and test data representing the center 850 ms of data. These waveforms are unfiltered, pressure corrected and sampled at 24 kHz.   

      For example: 

      Unzip the toadfishfinder_training_wavclips.zip file to reveal './bwhistle and './other' folders. 

      The readwavfiles.mlx (MATLAB) and readwavfiles.ipynb (PYTHON) scripts demonstrate how to load the data. If using MATLAB audioread function, specify 'native' format.