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DetEdit: A graphical user interface for annotating and editing events detected in long-term acoustic monitoring data

['Alba Solsona-Berga', 'Scripps Institution Of Oceanography', 'University Of California', 'San Diego', 'La Jolla', 'California', 'United States Of America', 'Kaitlin E. Frasier', 'Simone Baumann-Pickering', 'Sean M. Wiggins']

Date: None

The process of editing detections involves following several steps to create the parameters needed for the interface (Fig 1):

Step 1: Create LTSA files. The DetEdit package relies on mkLtsa to read audio files (wav format) and compress these data into long-term spectral averages (LTSA), which are power spectra calculated at regular intervals for the entire audio file [11]. LTSA are produced for the audio files by specifying the time average and frequency-bin size. They facilitate exploration and provide an easily visualized overview for long-term acoustic data.

Step 2: Create TPWS files. The input to the DetEdit GUI is a MATLAB binary file labeled as “TPWS” (Time, Peak-to-peak received levels, Waveform and Spectra parameters) that contains matrices of the acoustic detection parameters. These matrices can be created manually by the user or with make_TPWS which builds the following four primary variables to visualize detections in the interface:

If no detections are given, the package provides Edetect to assist in detecting acoustic events in audio files. This generic detector applies a configurable band pass filter and returns events that meet or exceed a minimum received level threshold and satisfy other configurable acoustic criteria.

Step 3: Create LTSA Sessions files. Detections are grouped in user-defined time bouts, defined as periods of stereotyped signals separated from prior and subsequent detections by a minimum specified time gap. mkLTSAsessions takes an LTSA and produces the following two variables needed to represent subsets of the LTSA per bout:

Visualization, annotation, and manipulation of data.

After building the parameter files, and specifying the input directories and display parameters in a script (see myDataSettings as an example), the user evokes the interface by calling detEdit. Predefined parameters for eleven species of odontocetes (e.g. beaked whale, dolphin, and sperm whale) are provided in initSpParams. The data are organized and displayed in bouts of detections allowing users to annotate large batches of detections (see https://github.com/MarineBioAcousticsRC/DetEdit/wiki/Getting-Started for more details). Seven panels are displayed to provide the signal detail and context needed to discriminate between different types of detections (Fig 2). The main interactive panel displays peak-to-peak received levels (RL pp dB re 1μPa) of detected signals, with the concurrent LTSA, and time between detections (time difference between one detected signal and the next). The concurrent waveforms, spectra, transformed version of root-mean-square (RL rms dB re 1μPa) and peak frequencies are displayed on additional interactive panels. RL rms summarizes the distribution of energy within a waveform, and is transformed to facilitate the annotation of signals of consistent shape but with varying amplitude (Fig 3A and 3B). Presuming that signals of a consistent shape will increase linearly in both RL pp and RL rms , the slope is arranged vertically, where transformed RL rms = RMS–correction factor* (RL pp −RL pp threshold) (Fig 3C and 3D). This transformation emphasizes values that deviate from the basic relationship. Generally for a vertical transformation when the increase in RL pp is the same as in RL rms , a correction factor equal to one is appropriate. Signal types that do not meet this linear increment required a different correction factor to enable the vertical arrangement.

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larger image TIFF original image Download: Fig 2. Visualization of acoustic data in the DetEdit interface. Seven panels are displayed with three over the event period: (A) RL pp , (B) LTSA, (C) time interval between detections; and four other showing various detection metrics and details: (D) normalized spectral density, (E) normalized waveforms, (F) RL pp versus transformed RL rms , and (G) peak frequencies versus transformed RL rms for sperm whale (Physeter macrocephalus) signal detections with true detections as blue, manually identified false detections of delphinid signals as red, and one detection using the selection tool displayed as black. All detections from the recording are shown in gray on the background if specified by the user to ease comparison of distributions across bouts. Customized classification thresholds are displayed as thin red lines (F and G). https://doi.org/10.1371/journal.pcbi.1007598.g002

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larger image TIFF original image Download: Fig 3. Conceptualization of transformed RL rms . Example signals of identical RL pp with low RMS indicating a signal characterized by few high amplitude cycles (A), and high RMS indicating a sustained signal with many cycles at high amplitude (B). (C) Signal A and B with varying RL pp plotted as a function of RL pp versus RL rms displaying a linear relationship, (D) vertical slope for transformed RL rms . https://doi.org/10.1371/journal.pcbi.1007598.g003

[1] Url: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007598

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