The File Analysis page provides a table-based interface for exploring and analyzing bioacoustic audio recording files. It aggregates observations by audio file and calculates metrics like number of observations per file, species diversity, and confidence scores. It generates a file quality Score for each file based on multiple factors and provides audio playback functionality. Users can explore files through this interactive data table and navigate to a detailed file analysis by double-clicking on rows the data table.
User Inputs (found on the sidebar for this tab)
| Input Name | Input Choices | Description |
| Model-Match Scale
‘Model Proximity’ or ‘Model Adherence’ |
Numerical value or range of values between 0 and 1. | This input allows you to filter that output on this tab based on BirdNET-Analyzer’s ‘Confidence’ attribute. We have renamed this input to ‘BirdNET Model-Match Scale’’ because BirdNET’s confidence isn’t actually a probability and is not related to confidence intervals. Read more about that here. |
| Species to Exclude | Multiple selection of species to exclude for generating the output table. | Observations for the selected species will not be included in the output data table. In other words, if an audio file only contains observations of the selected species, that audio file will not be included in the data table output. |
| Filter Files by Species | Multiple selection of species to use for filtering the output table. | Only files which contain at least one observation for the selected species will be included in the output. The selections in this input utilize “OR” logic. |
Output
Notes for Admins
- Two reactive inputs are created called `debounced_species_exclude` and `debounced_species_file_filter` for filtering files by specific species.
- The `cached_filtered_data` reactive value is updated whenever the filtered data or species exclusion criteria change. It removes excluded species from `filtered_data` and if species file filtering is applied, it identifies audio files containing those species and retains only observations from those files, effectively filtering the entire dataset by file-level species presence.
- Data processing begins in the `create_pivot_files` function, which groups observations by audio file (`Begin.Path`) along with location, date, and weather data. For each unique file, it calculates summary metrics including number of observations, number of unique species, mean confidence scores, and determines time of day categories (Morning/Afternoon/Night).
- Percentile based scoring is implemented that calculates percentile ranks for three metrics: number of observations, wind speed (inverted so lower wind scores higher), and mean confidence. These are averaged into a single `Score` value, wi5th missing wind speed handled by averaging the other two metrics plus 0.5. The score is then mapped to a color gradient from white (lowest) to dark blue (highest).
- Interactive audio buttons are generate for each file using the `find_audio_file_full` function, which locates the actual audio file path and creates a button that will stream the first 15 seconds of the audio file when clicked.
- The data table is rendered with customization including CSV and copy export buttons, fixed headers, scrolling, and conditional formatting that colors the Score column.
- Double-clicking a table row triggers navigation to a detailed file analysis view by updating the `file_click` reactive value with the selected file path.