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opennotspecifiedJun 2001View details →

Figs 1 - 3. The three graphic representation s in Audiospectrographical analysis of cicada sound production: a catalogue (Hemiptera, Cicadidae)

Figs 1 - 3. The three graphic representation s of sound: the calling song of the PalaearcticspeciesCicadaon i Linnaeus 1758, 1: oscillogram (amplitude vs time). 2: spectrogram or sonagram (frequenc y vs time). 3: spectrum (amplitude vs frequency). Sound analysis software: SYNTANA T. Aubin CNRS U A 149 1 (Aubin 1994).

opencc-by-4.0Nov 2023View details →

SELECTIVELY MANIPULATING SOFTNESS PERCEPTION OF MATERIALS THROUGH SOUND SYMBOLISM

Cross-modal interactions between auditory and haptic perception manifest themselves in language, such as sound symbolic words: crunch, splash, and creak. Several studies have shown strong associations between sound symbolic words, shapes (e.g., Bouba/Kiki effect), and materials. Here, we identified these material associations in Turkish sound symbolic words and then tested for their effect on softness perception. First, we used a rating task in a semantic differentiation method to extract the perceived softness dimensions from words and materials. We then tested whether Turkish onomatopoeic words can be used to manipulate the perceived softness of everyday materials such as honey, silk, or sand across different dimensions of softness. In the first preliminary study, we used 40 material videos and 29 adjectives in a rating task with a semantic differentiation method to extract the main softness dimensions. A principal component analysis revealed 7 softness components, including Deformability, Viscosity, Surface Softness, and Granularity, in line with the literature. The second preliminary study used 47 Turkish onomatopoeic words and 31 adjectives in the same rating task. Again, the findings aligned with the literature, revealing dimensions such as Fluidity, Granularity, and Surface Softness. However, no factors related to Deformability were found due to the absence of sound symbolic words in this category. Next, we paired the onomatopoeic words and material videos based on their associations with each softness dimension. We conducted a new rating task, synchronously presenting material videos and spoken onomatopoeic words. We hypothesized that congruent word-video pairs would produce significantly higher ratings for dimension-related adjectives, while incongruent word-video pairs would decrease these ratings, and the ratings of unrelated adjectives would remain the same. Our results revealed that onomatopoeic words selectively alter the perceived material qualities, providing evidence and insight into the cross-modality of perceived softness.

opencc-by-4.0Dec 2023View details →

Sound-source directivity dataset for four loudspekaers

A dataset of sound-source directivity measurements for four different sound sources that can be used in acoustic measurements.  The loudspeakers included: <ul> <li>Genelec 8030B studio monitor;</li> <li>Genelec 8331A studio monitor;</li> <li>01 dB LS01 omnidirectional loudspeaker conforming with ISO 3382-1:2009 standard (OmniSource);</li> <li>Mixed-order spherical loudspeaker type 393 (information on design of the loudspeaker is found in the publication: https://www.researchgate.net/publication/333132335_Design_and_Control_of_Mixed-Order_Spherical_Loudspeaker_Arrays). NOTE: the orientation of the drivers is specified in the table below.</li> </ul> <table> <tbody> <tr> <td>Elevation (deg.)</td> <td>Azimuth (deg.)</td> <td>Driver #</td> </tr> <tr> <td>0</td> <td>0</td> <td>15</td> </tr> <tr> <td>0 </td> <td>-40 (320)</td> <td>11</td> </tr> <tr> <td>0 </td> <td>-80 (280)</td> <td>10</td> </tr> <tr> <td>0</td> <td>-120 (240)</td> <td>9</td> </tr> <tr> <td>0</td> <td>-160 (200)</td> <td>8</td> </tr> <tr> <td>0</td> <td>40</td> <td>7</td> </tr> <tr> <td>0</td> <td>80</td> <td>6</td> </tr> <tr> <td>0</td> <td>120 </td> <td>5</td> </tr> <tr> <td>0</td> <td>160</td> <td>4</td> </tr> <tr> <td>-45</td> <td>120</td> <td>13</td> </tr> <tr> <td>-45</td> <td>0</td> <td>12</td> </tr> <tr> <td>-45</td> <td>-120 (240)</td> <td>14</td> </tr> <tr> <td>45</td> <td>-60 (300)</td> <td>2</td> </tr> <tr> <td>45</td> <td>60</td> <td>1</td> </tr> <tr> <td>45</td> <td>180</td> <td>3</td> </tr> </tbody> </table>   The directivity was measured in the anechoic chamber Lampio at the Acoustics Lab of Aalto University in Espoo, Finland. The measurement microphones were  G.R.A.S. 46AF 1/2" free-field microphones and B&K 4191 1/2" free-field microphone. All the receivers were fixed on a custom-made measurement arc. The sound sources were placed ona turntable allowing for high angular resolution in the XY-plane. More information about the measurements is available in the paper: Anthony Gallien, Karolina Prawda, and Sebastian J. Schlecht, "Matching early reflections of simulated and measured RIRs by applying sound-source directivity filters", in Proc. AES ASR 2024, 22-26 Jan. 2024, Le Mans, France Directivity plots for all sound sources are available online: http://research.spa.aalto.fi/publications/papers/aes-asr24-recreate-RIRs/  The work leading to producing the dataset and related research was conducted as Anthony Gallien's intership project at Aalto University Acoustics Lab from May 22 -- August 18, 2023.   <h3>How to read this dataset:</h3> The measurements of each sound-source are available as a Spatially Oriented Format for Acoustics (SOFA), AES69-2015 file or as a set of individual directivity measurements in .wav format included in a .zip file with the name of a respective sound source.  The naming convention for Genelec studio loudspeakers and 01 dB omnidirectional loudspeaker is: Calibrated_IR_elevation_E_azimuth_A.wav The naming convention for the mixed-order loudspeaker is: Calibrated_IR_elevation_E_azimuth_A_driver_D.wav NOTE that directivity measurements for mixed-order loudspeaker and elevation above 0 degrees are missing due to a loudspeaker-related technical issue during the measurement!   The azimuth and elevation angles are determined according to the scheme presented in the file azimuth_elevation.pdf. Where elevation is relative to XY-plane at the horizontal axis of the sound source and azimuth is relative to the vertical plane at the vertical axis of the loudspekaer.  The elevation angles range from -80 degrees to 90 degrees in 10-degree steps. The azimuth angles range from 0 degrees to 359 degrees in 1-degree steps for Genelec loudspekers and omnidirectional sound source, and in 5-degree steps for the mixed-order loudspeakers. The azimuth angles increase clockwise relative to the axis of the loudspeaker (1 degree azimuth means 1 degree right of the loudspeaker axis, 359 degree azimuth means 1 degree left of the loudspeaker axis).   The datset includes a total of 38 880 measurements (6 480 measurements for omnidirectional source and each of the Genelec loudspeakers, 19 440 measurements for the mixed-order loudspeaker).

opencc-by-nc-4.0Dec 2023View details →

TAU Sound Events and Speech Privacy Preservation

<div>The TAU Sound Events and Speech Privacy Preservation Dataset is a collection of audio data used in the work "Adversarial Representation Learning for Robust Privacy Preservation in Audio" by S. Gharib, M. Tran, D. Luong, K. Drossos, and T. Virtanen. The dataset is created by merging subsets of the <a href="../records/4060432">Freesound 50k Dataset (FSD50K)</a> and the <a href="https://www.openslr.org/12">LibriSpeech corpus</a>. Both FSD50K and LibriSpeech are licensed under the Creative Commons license.</div> <div>The dataset contains of ~5000 one-second sound event samples with or without speech content provided in WAV and NumPy array format (approximately half of the samples contains speech). The creation of the dataset ensures an equal number of samples for male and female speakers across each sound event class. The sound event classes included in this dataset are:</div> <ul> <li>dog barking</li> <li>glass breaking</li> <li>gun shot</li> <li>cough</li> <li>slam</li> <li>applause</li> <li>dished pot pan</li> <li>toilet flush</li> <li>cat meowing</li> <li>doorbell</li> <li>crying</li> <li>drill</li> </ul> Please check the README for better understanding of the dataset.

opencc-by-4.0Dec 2023View details →

Call Graph Soundness in Android Static Analysis

<div> <pre>## Artifact Folder Structure Below is a brief explanation of each directory of our artifacts: - **dataset/**: Contains the dataset used in our study. - **dynamic_analysis/**: Contains everything needed to reproduce our dynamic analysis experiments. - **static_analysis/**: Contains everything needed to reproduce our static analyses experiments. - **instrumentation/**: Contains the necessary files to instrument the apps for the dynamic analysis. - **SLR/**: Contains the excel files with the papers collected during our Systematic Literature Review (SLR). Please ensure that all the necessary files and resources are present in the respective directories before running any experiments.</pre> </div>

opencc-byMay 2015View details →

Elastolin indian horseman (with sound)

Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Jan 2024View details →

Yangjiang sounding data

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2024View details →

The Sounds of Home (labels only)

The Household Sounds dataset consists of recordings from 14 microphones, each housed in its own folder. Here you will find only the automatically generated predictions. You can find the complete dataset at https://zenodo.org/records/12737915

opencc-by-4.0Jul 2024View details →

Responses, datasets used from the Geomagnetic Network of China, and the 3D inversion result of Geomagnetic depth sounding

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opencc-by-4.0Sep 2021View details →

Implications of sound velocities of natural topaz on the seismic L-discontinuity

This is the data for the paper "Implications of sound velocities of natural topaz on the seismic L-discontinuity".

opencc-by-4.0Dec 2021View details →

Data and derived products from airborne radar sounding survey over Devon Ice Cap, Canadian Arctic

Data and derived products used in Rutishauser et al., “Radar sounding survey over Devon Ice Cap indicates the potential for a diverse hypersaline subglacial hydrological environment”, accepted for publication, The Cryosphere, <a href="https://doi.org/10.5194/tc-2021-220">https://doi.org/10.5194/tc-2021-220</a> Corresponding author: <a href="mailto:rutishauser.anja@gmail.com">rutishauser.anja@gmail.com</a>   Description of datasets: ------------------------------------------ 2018_DIC_UTIG.IR2HI1B.kml Geolocation of the SRH1 profile lines. Coordinates in EPSG: 4326 - WGS 84 (latitude, longitude) ------------------------------------------ 2018_DIC_UTIG.IR2HI1B.tgz HiCARS 2 L1B echo strength profiles (radargrams) in NetCDF format. The naming of the files has the structure IR2HI1B_YYYYDOY_PST_x (e.g. IR2HI1B_2018153_DEV_JKB2t_Y87b_000.nc), where YYYY is the survey year (e.g. 2018), DOY is the survey day of the year (e.g. 153), PST is the profile name (e.g. DEV_JKB2t_Y87b), and x is the segment number if the profile was split in two (e.g. 000). For each profile, a PDF file showing the profile location and the radargram is included. ------------------------------------------ 2018_DIC_UTIG.Level2.tgz  Level 2 datasets for each profile, organized in the following folders: <ul> <li>2018_DIC_UTIG.ILUTP2: Laser altimeter geolocated surface elevation</li> <li>2018_DIC_UTIG.IR2HI2: HiCARS 2 unfocused (pik1) geolocated ice thickness, ice surface elevation, bed elevation, surface- and bed reflection coefficients, and aircraft roll</li> <li>2018_DIC_UTIG.IRHFOC2: HiCARS 2 focused (foc1) geolocated ice thickness, ice surface elevation, bed elevation, surface- and bed reflection coefficients, and aircraft roll</li> <li>2018_DIC_UTIG.IRSPC2: HiCARS 2 derived basal interface specularity content</li> </ul> ------------------------------------------ Devon_basal_ice_temperature.tif: Modeled basal ice temperature [ºC] using a 1D advection diffusion model. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_bedrockDEM.tif: Digital elevation model (DEM) of the bedrock topography beneath Devon Ice Cap [m asl.]. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_gridded_RMSD_bedrock.tif: Root mean square deviation (RMDS) of the bedrock topography [m]. The RMSD was computed along each profile line, then interpolated on a 500x500m grid using the QGIS GDAL moving average grid interpolation. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_gridded_specularity.tif: Specularity content from along the profile lines interpolated on a 500x500m grid using the QGIS GDAL moving average grid interpolation. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_ice_thickness.tif: Gridded ice thickness [m] generated by subtracting the bedrock DEM from ice surface elevations derived from the ArcticDEM, Polar Geospatial Center from DigitalGlobe Inc. imagery. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_modeled_Geology.zip <ul> <li>Devon_modeled_subglacial_geology.tif: Map of the projected geological units beneath Devon Ice Cap. The assigned numbers correspond to the following geological units: 1: pPe, 2: Cm-cf, 3: Oe, 4: Ocb, 5: Oct. Details on the geological units can be found in (Harrison et al., 2016; Mayr, 1980; Thorsteinsson & Mayr, 1987). 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N.</li> <li>{pPe, Oe, Oct, Ocb,Cm_rb}_Model.stl: 3D geometry of the modeled geological units beneath Devon Ice Cap, originally published in (Rutishauser et al., 2018). Coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N.</li> <li>Devon_load_geology_stl_files.py: Python script to load and plot the 3D geology layers in the .stl files.</li> </ul> ------------------------------------------ Devon_subgl_hydraulic_head.tif: Subglacial hydraulic head [m] beneath Devon Ice Cap. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_subgl_hydraulic_slope.tif Slope [º] of the subglacial hydraulic head beneath Devon Ice Cap. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N. ------------------------------------------ Devon_subgl_lakes_brine_network.zip <ul> <li>Devon_subgl_lake_outline.shp: Shoreline of the subglacial lakes beneath Devon Ice Cap (identified in this study). Coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N.</li> <li>Devon_brine_network_outline.shp: Outlines of the mapped subglacial brine network beneath Devon Ice Cap. Coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N.</li> </ul> ------------------------------------------ Devon_subgl_water_routes.zip <ul> <li>Devon_modeled_subgl_water_routes_{1, 2, 3}std.tif: Modeled subglacial water routes derived via application of a flow accumulation algorithm to the hydraulic head. The model is run 1000 times with normally distributed random errors of 1, 2 and 3 standard deviations of the hydraulic head uncertainty added to the hydraulic head (represented in the file name). Pixel values represent the model counts for which the cell has a minimum of 10 upstream cells draining into it. 500 m grid cell size, coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N.</li> <li>Devon_extracted_subgl_water_routes.shp: Modeled subglacial water routes derived via the application of a flow accumulation algorithm to the hydraulic head. Coordinates in EPSG: 32617 - WGS 84 / UTM zone 17N.</li> </ul> ------------------------------------------ SpecularityJustification.zip Jupyter notebook and example datafile to show the justification for the chosen specularity threshold of 0.4 for declaring a detection of possible subglacial water. ------------------------------------------   Acknowledgments The aerogeophysical survey and subsequent standard data processing were funded by the Weston Family Foundation. We also thank the G. Unger Vetlesen Foundation and the UTIG Postdoctoral Fellowship program who provided further funding for data analysis. M.L.S. was partially supported by NASA NNX16AJ64G and NASA 80NSSC20K1134. We thank PCSP and Kenn Borek Air Ltd. for logistical support, and the Nunavut Research Institute and the peoples of Grise Fjord and Resolute Bay for permission to conduct airborne surveys over Devon Ice Cap. Finally, we thank Scott Kempf for assistance with data processing, and Sam Christian and Miguel Liu-Schiaffini for help with radar reflection picking. References Harrison, J. C., Lynds, T., Ford, A., & Rainbird, R. H. (2016). Geology, simplified tectonic assemblage map of the Canadian Arctic Islands, Northwest Territories - Nunavut. Geological Survey of Canada, Canadian Geoscience, Map 80. https://doi.org/10.4095/297416 Mayr, U. (1980). Stratigraphy and correlation of lower Paleozoic formations, subsurface of Bathurst Island and adjacent smaller islands, Canadian Arctic Archipelago. Geological Survey of Canada, Bulletin, 306. https://doi.org/10.4095/102157 Rutishauser, A., Blankenship, D. D., Sharp, M., Skidmore, M. L., Greenbaum, J. S., Grima, C., Schroeder, D. M., Dowdeswell, J. A., & Young, D. A. (2018). Discovery of a hypersaline subglacial lake complex beneath Devon Ice Cap, Canadian Arctic. Science Advances, 4(4), eaar4353. https://doi.org/10.1126/sciadv.aar4353 Thorsteinsson, R., & Mayr, U. (1987). The sedimentary rocks of Devon island, canadian arctic archipelago. https://doi.org/10.4095/122451

opencc-zeroJan 2022View details →

Coastal infrastructure alters behavior and increases predation mortality of threatened Puget Sound steelhead smolts

Fundamental movements of migratory species can be substantially influenced by marine habitat disruptions caused by coastal infrastructure. The Hood Canal Bridge (HCB) spans the northern outlet of Hood Canal in the Salish Sea, extends 4.6 meters (15 ft) underwater, and forms a partial barrier for steelhead migrating from Hood Canal to the Pacific Ocean. Spatial mark-recapture survival models using acoustic telemetry data indicate that only 49% (2017; 95% CI = 40, 58%) and 56% (2018; 95% CI = 48, 65%) of the steelhead smolts encountering the HCB survived past the bridge and 7 km to the next array. We studied fine-scale movements of more than 300 steelhead smolts to understand how migration behavior was affected across the entire length of the HCB and to quantify spatial and temporal patterns of mortality. Individually coded acoustic telemetry transmitters implanted in juvenile steelhead were used in conjunction with an extensive array of acoustic receivers surrounding the HCB to obtain approximations of the path each steelhead took as they encountered the bridge structure. Steelhead survival past the HCB appeared unaffected by tidal stage, population-of-origin, approach location, current velocity, or time of day, but was influenced by week of bridge encounter. Behavioral data from transmitters with temperature and depth sensors ingested by predators are consistent with high levels of marine mammal predation. This study confirms the considerable impact of the HCB on ESA-listed steelhead smolt survival, and provides detailed information on the behavior of steelhead smolts and their predators at the HCB for use in planning recovery actions.

opencc-zeroJan 2022View details →

High pitch sounds small for domestic dogs dataset

Humans possess intuitive associations linking certain non-redundant features of stimuli – e.g., high-pitched sounds with small object-size (or similarly, low-pitched sounds with large object-size). This phenomenon, known as crossmodal correspondence, has been identified in humans across multiple different senses. There is some evidence that non-human animals also form crossmodal correspondences, but the known examples are mostly limited to the associations between the pitch of vocalisations and the size of callers. To investigate whether domestic dogs, like humans, show abstract pitch-size association, we first trained dogs to approach and touch an object after hearing a sound emanating from it. Subsequently, we repeated the task but presented dogs with two objects differing in size, only one of which was playing a sound. The sound was either high- or low-pitched, thereby creating trials that were either congruent (high-pitch from small object; low-pitch from large objects) or incongruent (the reverse). We found that dogs reacted faster on congruent versus incongruent trials. Moreover, their accuracy was at chance on incongruent trials, but significantly above chance for congruent trials. Our results suggest that non-human animals show abstract pitch-sound correspondences, indicating these correspondences may not be uniquely human but rather a sensory processing feature shared by other species.

opencc-zeroFeb 2022View details →

Automated bird sound classifications of long-duration recordings produce occupancy model outputs similar to manually annotated data

Occupancy modeling is used to evaluate avian distributions and habitat associations, yet it typically requires extensive survey effort because a minimum of three repeat samples are required for accurate parameter estimation. Autonomous recording units (ARUs) can reduce the need for surveyors on site, yet ARUs utility were limited by hardware costs and the time required to manually annotate recordings. Software that identifies bird vocalizations may reduce expert time needed, if classification is sufficiently accurate. We assessed the performance of BirdNET – an automated classifier capable of identifying vocalizations from >900 North American and European bird species – by comparing automated to manual annotations of recordings of 13 breeding bird species collected in northwestern California. We compared the parameter estimates of occupancy models evaluating habitat associations supplied with manually annotated data (9 min recording segments) to output from models supplied with BirdNET detections. We used three sets of BirdNET output to evaluate the duration of automatic annotation needed to approach manually annotated model parameter estimates: 9-min, 87-min, and 87-min of high-confidence detections. We incorporated 100 3-sec manually validated BirdNET detections per species to estimate true and false positive rates within an occupancy model. BirdNET correctly identified 90% and 65% of the bird species a human detected when data were restricted to detections exceeding a low or high confidence score threshold, respectively. Occupancy estimates, including habitat associations, were similar regardless of method. Precision (proportion of true positives to all detections) was >0.70 for 9 of 13 species, and a low of 0.29. However, processing of longer recordings was needed to rival manually annotated data. We conclude that BirdNET is suitable for annotating multispecies recordings for occupancy modeling when extended recording durations are used. Together, ARUs and BirdNET may benefit monitoring and, ultimately, conservation of bird populations by greatly increasing monitoring opportunities.

opencc-by-4.0Feb 2022View details →

Acoustic propulsion of nano- and microcones: dependence on particle size, acoustic energy density, and sound frequency

Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Acoustic propulsion of nano- and microcones: dependence on particle size, acoustic energy density, and sound frequency".

opencc-zeroMar 2022View details →

Changes in sea ice and range expansion of sperm whales in the eclipse sound region of Baffin Bay, Canada

Sperm whales (Physeter macrocephalus) are a cosmopolitan species but are only found in ice-free regions of the ocean. It is unknown how their distribution might change in regions undergoing rapid loss of sea ice and ocean warming like Baffin Bay in the eastern Canadian Arctic. In 2014 and 2018, sperm whales were sighted near Eclipse Sound, Baffin Bay: the first recorded uses of this region by sperm whales. In this study, we investigate the spatiotemporal distribution of sperm whales near Eclipse Sound using visual and acoustic data. We combine several published open-source, data sets to create a map of historical sperm whale presence in the region. We use passive acoustic data from two recording sites between 2015 and 2019 to investigate more recent presence in the region. We also analyze regional trends in sea ice concentration dating back to 1901 and relate acoustic presence of sperm whales to the mean sea ice concentration near the recording sites. We found no records of sperm whale sightings near Eclipse Sound outside of the 2014/2018 observations. Our acoustic data told a different story, with sperm whales recorded yearly from 2015-2019 with presence in the late summer and fall months. Sperm whale acoustic presence increased over the 5-year study duration and was closely related to the minimum sea ice concentration each year. Sperm whales, like other cetaceans, are ecosystem sentinels, or indicators of ecosystem change. Increasing number of days with sperm whale presence in the Eclipse Sound region could indicate range expansion of sperm whales as a result of changes in sea ice. Monitoring climate change-induced range expansion in this region is important to understand how increasing presence of a top-predator might impact the Arctic food web.

FIGURE 14 in Review of song patterns and sound production in armoured ground crickets (Orthoptera: Tettigoniidae: Hetrodini) with karyological data and taxonomic notes

FIGURE 14. Example of three syntopic Hetrodini species (Rau Forest, from left: Spalacomimus talpa, Enyaliopsis ephippiatus, Eugasteroides loricatus) and Spalacomimus stettinensis (second from right), occurring nearby (all 1 Nov 2021).

FIGURE 12 in Review of song patterns and sound production in armoured ground crickets (Orthoptera: Tettigoniidae: Hetrodini) with karyological data and taxonomic notes

FIGURE 12. Examples of chromosome number and heterochromatin C-banding of mitotic male (A–C, G–K) and mitotic female complements (D), as well as diplotene/diakinesis (C, E, I on the right), and metaphase I (F) for the following Hetrodini taxa: A Cosmoderus femoralis, 2n = 24 + X0 (CH8626), B Enyaliopsis bloyeti, 2n = 28 + X0 (HE114), C Enyaliopsis carolinus, 2n =24 + neo-XY (CH8625), D Enyaliopsis ephippiatus, 2n = 26 + XX (female CH7849), E Enyaliopsis jennae, 2n = 26 + neo-XY (HE87), F Enyaliopsis spec. 2 Mpwapwa, 2n = 26 + X0 (CH8353), G Gymnoproctus rammei, 2n = 26 + X0 (CH8763), H Gymnoproctus spec., 2n = 26 + X0 (CH7953), I Spalacomimus magnus, 2n = 16 + X0 (CH7945), J Spalacomimus verruciferus, 2n = 22 + neo-XY (CH7897), K Spalacomimus spec. near verruciferus, 2n = 22 + neo-XY (CH7899). Bi-armed chromosomes (meta/submeta/subacrocentric) are marked with numbers (A, C, D, F–I); in the neo-XY system, sex chromosomes form association in diplotene (C) and metaphase I (I); arrowheads indicate interstitial C-bands in 1 (A, E, J, K) and 2 (I) pairs; asterisks (*) marked distal C-bands in 3, 4, 5 (C), 3, 4 (D), 2-heteromorphic (G, H) pair; X = neo-X and Y = neo-Y sex chromosomes. Scale bar = 10 µm.

FIGURE 9 in Review of song patterns and sound production in armoured ground crickets (Orthoptera: Tettigoniidae: Hetrodini) with karyological data and taxonomic notes

FIGURE 9. Titillator and genitalic sclerites in Hetrodini, Enyaliopsina. First row A Cosmoderus femoralis (CH8627), B Enyaliopsis bloyeti (Kazimzumbwi), C Enyaliopsis carolinus (Minziro), D Enyaliopsis ephippiatus (Rau Forest); second row E Enyaliopsis spec. 1 near ephippiatus (Mwala), F Enyaliopsis jennae (Uluguru), G Enyaliopsis spec. 2 (Mpwapwa), H Enyaliopsis spec. 3 (East Chenene); third row I Gymnoproctus rammei (Puge Simbo), J Gymnoproctus spec. (East Chenene).

FIGURE 10 in Review of song patterns and sound production in armoured ground crickets (Orthoptera: Tettigoniidae: Hetrodini) with karyological data and taxonomic notes

FIGURE 10. Oscillograms of the calling songs in the genera Eugaster, Eugasteroides and Spalacomimus (figures of S. liberianus based on figures and data from literature; see text). A Eugaster guyoni, B Eugaster spinulosa, C Eugasteroides loricatus, D Spalacomimus liberianus, E Spalacomimus magnus, F Spalacomimus stettinensis, G Spalacomimus talpa, H Spalacomimus verruciferus, I Spalacomimus spec. near verruciferus.