DOI: 10.5061/dryad.qrfj6q5tb
title: Bird species classifications from Munich-Laim: Expert vs. BirdNET (multi-parameter) results

description: This dataset contains expert annotations and BirdNET-generated species
 detections from one-minute audio recordings collected at a single urban
 site in Laim, Munich, Germany, between May and October 2021. Recordings
 were made in timed intervals during the early morning hours to focus on
 peak bird vocal activity, resulting in 15.5 hours of audio from 60
 recording days. Each recording was manually reviewed by two experienced
 ornithologists for bird vocalisations, and the same audio was processed
 using BirdNET-Analyzer (v2.2, April 2023) across a range of parameter
 combinations, including variations in sensitivity, overlap, week of year,
 and confidence threshold. The dataset includes raw BirdNET outputs for all
 parameter combinations as well as expert-verified species lists for each
 clip.
description: Acoustic
 recordings Between May and October 2021, we deployed a
 single Frontier Labs BAR recorder on the roof of a residential building in
 Munich, Laim, Germany. Recordings were made in week-long sessions, with at
 least a one-week break between each session. During each session, the
 recorder was programmed to capture one-minute clips every ten minutes,
 starting two hours before sunrise and continuing until three hours after
 sunrise. All recordings were made at 48 kHz sampling rate, 16-bit depth,
 and with a gain setting of 40 dB. Species
 Identification Two experienced ornithologists reviewed
 each one-minute clip to identify bird vocalisations, either by listening
 or by inspecting the spectrograms. This was done using Wildlife Acoustics
 Kaleidoscope Pro (version 5.6.8), with default settings (FFT size: 256,
 Window size: 128, Max cache: 256 MB). Each recording was then annotated
 with the species present. In addition, we processed the same audio files
 using BirdNET-Analyzer (April 2023 release, model v2.2), generating a list
 of species detected by the algorithm for each clip. 
 BirdNET parameter
 settings We generated BirdNET detections using
 different parameter combinations. First, we included or excluded the week
 of the year, which allows BirdNET to filter expected species based on date
 and location using eBird data. Second, we varied the sensitivity
 parameter, which affects how likely BirdNET is to detect quieter or
 background vocalisations. We used three levels: 0.5 (low), 1.0 (default),
 and 1.5 (high). Third, because BirdNET processes audio in three-second
 segments, we used four different overlap settings (0, 1, 2, and 2.9
 seconds) to control how much of the previous segment is reused. Finally,
 BirdNET assigns a confidence score to each detection. We set the minimum
 confidence threshold to the default (0.1) to retain all
 detections. The code for producing the analyses found
 in the associated manuscript are on GitHub: https://github.com/AFairbairn/BirdNET_study_PLOS_one
description: # Bird Detection Data This dataset contains expert annotations and
 BirdNET-generated species detections from one-minute audio recordings
 collected at a single urban site in Laim, Munich, Germany, between May and
 October 2021. Recordings were made in timed intervals during the early
 morning hours to focus on peak bird vocal activity, resulting in 15.5
 hours of audio from 60 recording days. Each recording was manually
 reviewed by two experienced ornithologists for bird vocalisations, and the
 same audio was processed using BirdNET-Analyzer (v2.2, April 2023) across
 a range of parameter combinations, including variations in sensitivity,
 overlap, week of year, and confidence threshold. The dataset includes raw
 BirdNET outputs for all parameter combinations as well as expert-verified
 species lists for each clip and the confirmed detections for the best
 result. ## Files ### `birdnet.csv` Automated bird species identification
 results generated by BirdNET software. **Columns:** * `filename`: Audio
 recording filename (format: RECORDINGNAME_YYYYMMDD_HHMMSS_LOCATION.wav) *
 `scientific_name`: Scientific name of detected species * `common_name`:
 Common/vernacular name of detected species * `confidence`: BirdNET
 confidence score (0.1-1) for the detection * `week`: Logical indicator for
 week included, not included * `overlap`: Numeric value indicating overlap
 (in seconds) * `sensitivity`: BirdNET sensitivity setting used (0-1) *
 `date`: Recording date (YYYY-MM-DD format) * `session`: Recording session
 number (corresponds to week of recording) * `start_time`: Recording start
 time (HH:MM:SS format) ### `expert.csv` Manual bird species identification
 results performed by expert ornithologists. **Columns:** * `filename`:
 Audio recording filename (format:
 RECORDINGNAME_YYYYMMDD_HHMMSS_LOCATION.wav) * `scientific_name`:
 Scientific name of identified species * `session`: Recording session
 number (corresponds to week of recording) * `date`: Recording date
 (YYYY-MM-DD format) * `start_time`: Recording start time (HH:MM:SS format)
 ### `confirmed_results.csv` Confirmed results from the dataset level.
 **Columns:** * `scientific_name`: Scientific name of identified species *
 `MANUAL ID`: T for true positives, F for false positives * `n`: Total
 number of true or false positives per species