DOI: 10.5061/dryad.kd51c5bgg
title: Data from: Innovative microphone transmitter reveals differences in acoustic structure between broadcast and whisper songs of Myadestes obscurus (ʻŌmaʻo)

description: Low-amplitude ‘whisper songs’ are a taxonomically broad phenomenon in
 birds that could play an important role in the suite of behaviors birds
 use to communicate. Due to its cryptic nature, there are inherent
 difficulties in capturing high-quality whisper song recordings without
 interrupting natural behaviors. Thus, whisper song acoustic structure is
 poorly understood and its potential function remains the subject of
 debate. Here, we present one of the first quantitative assessments of the
 acoustic structure of whisper song in birds. Using an innovative
 microphone transmitter we collected high quality recordings of broadcast
 and whisper songs from the Myadestes obscurus (ʻŌmaʻo), a thrush species
 endemic to the Island of Hawai‘i. The transmitter was attached to the
 birds and broadcasted radio-signals of all vocalizations produced by the
 individual to distances over 100 m away which minimized disruption of the
 birds’ normal behavior while recording. We demonstrate that ʻŌmaʻo whisper
 songs are a distinct class of vocalization that differ from broadcast
 songs in acoustic characteristics beyond amplitude, such as song length,
 frequency, and length of silent intervals between notes. These findings,
 in conjunction with habitat-associated variation in the rate at which
 ʻŌmaʻo emit these vocalization classes, indicate broadcast and whisper
 songs likely serve separate functions. This work provides evidence
 supporting the ‘acoustic adaptation hypothesis’ which posits that densely
 vegetated habitats promote the evolution of songs with specific acoustic
 features that maintain signal integrity as the sound propagates through
 the environment.
description: We equipped ʻŌmaʻo with a device that consisted of a very high
 frequency (VHF) radio pulse transmitter and a miniature condenser
 microphone that broadcasted all sounds emitted by the target bird (JDJC
 Corporation, Fisher, IL, USA). Radio signals from the microphone
 transmitter were received by a hand-held 3-element yagi antenna (JDJC
 Corporation, Fisher, IL, USA) connected to a wideband receiver (AR8200;
 AOR Ltd., Tokyo, Japan) in wideband amplitude modulation (WAM) mode. The
 signals were recorded by a Marantz PMD661 professional field recorder
 (Marantz America, LLC., Mahwah, NJ, USA), using a 2-second pre-record
 setting, as 24-bit WAV files at a 44.1 kHz sampling rate. The distance at
 which the microphone transmitter could be detected varied by terrain and
 weather, and birds recorded in our study were typically at a distance of
 &lt; 80 m. Each bird was tracked 3–5 days a week, excluding rainy
 days, for 2–4 hours between 0800–1600, which encompasses the peak hours
 for ʻŌmaʻo vocalizations. <br>Recordings were processed using the
 bioacoustic software Luscinia (v2.16.10.29.0). Songs were delineated by
 &gt; 5 s of silence between vocalization bouts and classified as
 either broadcast or whisper songs based on field observations, with
 verification through spectrogram visualization. Songs were then segmented
 into syllables, delineated by &gt; 0.2 s of silence between notes in a
 song, and syllables were further segmented into notes, delineated by any
 break in continuous sound production. In Luscinia, frame length was set to
 5 ms, time step was set to 1.1 ms, maximum frequency was set to 8 kHz, and
 the Hamming windowing function was selected before each note contour
 (vectors of parameter measurements with one entry for each time step in
 the spectrogram) was manually traced with a brush size of 5
 pixels. <br>To determine the number of unique syllables associated
 with broadcast and whisper songs for each bird, we used Luscinia’s dynamic
 time warping (DTW) algorithm. The DTW algorithm compares the structure of
 each syllable with every other syllable based on the Euclidean distance
 between acoustic features, producing a matrix of syllable dissimilarities.
 To achieve optimal syllable groupings, DTW acoustic parameters were
 weighted differently for broadcast and whisper songs. We clustered
 syllables using unweighted pair group method with arithmetic mean (UPGMA)
 hierarchical clusterings of the dissimilarity scores from the DTW
 analysis, assigning syllables to groups based on the resulting dendrogram.
 We calculated the global silhouette index, a measure of clustering
 tendency, at each depth within the dendrogram and identified natural
 clusters among the syllables by searching for the highest peak in the
 index. The syllable groupings were then visually and aurally validated and
 reassigned as necessary by an avian acoustics specialist. Although
 different syllables were incorrectly assigned to the same group &lt;
 1% of the time, similar syllables were frequently separated into different
 groups and needed manual grouping. Syllables were classified into separate
 groups if they had two or more differences in the number of notes or note
 shape.
description: # Data from: Innovative microphone transmitter reveals differences in
 acoustic structure between broadcast and whisper songs of Myadestes
 obscurus (ʻŌmaʻo)
 [https://doi.org/10.5061/dryad.kd51c5bgg](https://doi.org/10.5061/dryad.kd51c5bgg) ## Description of the data and file structure ### Files and variables #### File: OMAO.Whisper.Songs.DB.luscdb.zip **Description:** Luscinia formatted database files for the whisper songs collected during this study. The files can be opened and analyzed using Luscinia bioacoustic software by unzipping the file, opening Luscinia, then navigating the "Database location:" search box on the Log In page to the directory containing the OMAO.Whisper.Songs.DB.h2.db and OMAO.Whisper.Songs.DB.trace.db files (described below). It is important to navigate to the parent directory, and not the database files themselves, as they will not open independently.  ##### Subfile: OMAO.Whisper.Songs.DB.h2.db **Description:** Whisper song recording database formatted for analysis in Luscinia using the H2 Database Engine. It stores audio, metadata, and analytical results related to the bioacoustic data processed during this study. This database can be opened, inspected, and analyzed in Luscinia, although the acoustic measurements reported in the manuscript have already been exported and provided in LusciniaOutput.zip. Key data stored in this file include: * **Individuals**: Metadata about recorded subjects. * **Recordings**: Information about the audio files analyzed, such as file names, dates, and associated metadata. * **Vocalizations**: Data about detected vocal events, including timestamps, syllables, and notes. * **Measurements**: Spectral and temporal measurements of vocalizations, such as frequency, duration, etc. ##### Subfile: OMAO.Whisper.Songs.DB.trace.db **Description:** Luscinia's log file for the whisper song database, a record of the software’s execution and processes during its analysis of audio data, containing detailed information about the acoustic features of vocalizations analyzed. The trace.db file is organized into multiple tables, each storing specific aspects of the acoustic data. Key tables include: * **Individuals:** Information about the individuals whose vocalizations were analyzed, including metadata like species, recording date, and location. * **Recordings:** Details about the recordings, such as file names, duration, and associated metadata. * **Syllables and Notes:** Acoustic features of individual vocal units (e.g., peak frequency, duration, etc.). * **Measurements:** Quantitative features extracted from the spectrograms of vocalizations. #### File: OMAO.Broadcast.Songs.DB.luscdb.zip **Description:** Luscinia formatted database files for the broadcast songs collected during this study. The files can be opened and analyzed using Luscinia bioacoustic software by unzipping the file, opening Luscinia, then navigating the "Database location:" search box on the Log In page to the directory containing the OMAO.Broadcast.Songs.DB.h2.db and OMAO.Broadcast.Songs.DB.trace.db files (described below). It is important to navigate to the parent directory, and not the database files themselves, as they will not open independently.  ##### Subfile: OMAO.Broadcast.Songs.DB.h2.db **Description:**  Broadcast song recording database formatted for analysis in Luscinia using the H2 Database Engine. It stores audio, metadata, and analytical results related to the bioacoustic data processed during this study. This database can be opened, inspected, and analyzed in Luscinia, although the acoustic measurements reported in the manuscript have already been exported and provided in LusciniaOutput.zip. Key data stored in this file include: * **Individuals**: Metadata about recorded subjects. * **Recordings**: Information about the audio files analyzed, such as file names, dates, and associated metadata. * **Vocalizations**: Data about detected vocal events, including timestamps, syllables, and notes. * **Measurements**: Spectral and temporal measurements of vocalizations, such as frequency, duration, etc. ##### Subfile: OMAO.Broadcast.Songs.DB.trace.db **Description:** Luscinia's log file for the broadcast song database, a record of the software’s execution and processes during its analysis of audio data, containing detailed information about the acoustic features of vocalizations analyzed. The trace.db file is organized into multiple tables, each storing specific aspects of the acoustic data. Key tables include: * **Individuals**: Information about the individuals whose vocalizations were analyzed, including metadata like species and recording date. * **Recordings**: Details about the recordings, such as file names, duration, and associated metadata. * **Syllables and Notes**: Acoustic features of individual vocal units (e.g., peak frequency, duration, etc.). * **Measurements**: Quantitative features extracted from the spectrograms of vocalizations. #### File: LusciniaOutput.zip **Description:** A collection of Microsoft Excel files with two kinds of data for each of the 8 ʻŌmaʻo used in the study. File names indicate individual birds (OMAO_05), whether it is broadcast (BS) or whisper song (WS) data, and which collection of data it contains, either output from Luscinia's acoustic analysis (Parameters), or syllable grouping libraries (SyllableLib). These data files can be read into R using the OmaoCleanCode.R script to reproduce the results reported in the manuscript. * Syllable library files have the following columns  * Individual - which individual bird produced the songs, with a short numerical identifier, and the full identifier (as found in Table 1), separated by an underscore. * Names - an identifier for a each syllable, values contain the individual bird's short identifier number, the date of the recording, the source file name, the song number the syllable comes from, and the syllable number within the song. * LucGroup - Luscinia's assigned syllable grouping. * Group - manually adjusted syllable grouping (used for analysis). * Notes - annotator's notes, these indicate when a faint note was not traced in Luscinia. * Parameter files have the following measurements derived in Luscinia (details on measurements can be found on the Luscinia website: ([https://rflachlan.github.io/Luscinia/](https://rflachlan.github.io/Luscinia/)) * Song name * Syllable Number * Element Number * Record time * Record date * Start time * Length * Gap before * Peak frequency Mean * Peak frequency Maximum * Peak frequency Minimum * Median frequency Mean * Median frequency Maximum * Median frequency Minimum * Fundamental frequency Mean * Fundamental frequency Maximum * Fundamental frequency Minimum * Peak frequency change Mean * Peak frequency change Maximum * Peak frequency change Minimum * Wiener entropy Mean * Wiener entropy Maximum  * Wiener entropy Minimum * Frequency bandwidth Mean * Frequency bandwidth Maximum * Frequency bandwidth Minimum ## Code/software **Required software:** * R version 4.2 or above ([https://www.r-project.org/](https://www.r-project.org/)) * Luscinia version 2.16.10.29.0 or above ([https://rflachlan.github.io/Luscinia/](https://rflachlan.github.io/Luscinia/)) **Code:** * pdfa_functions.r - functions for running a permuted discriminant function analysis on nonindependent data in R (Mundry R., and C. Sommer (2007). Discriminant function analysis with nonindependent data: Consequences and an alternative. Animal Behaviour 74:965–976.) * OmaoCleanCode.R - an R script for compiling the Luscinia data for each bird, running the acoustic analyses described in the manuscript, and data visualization.