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FIGURES 104–122 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURES 104–122. Spectrogram of the calling song of crickets. 104–107, Sonograms of the crickets Mogoplistidae; 108–122, Sonograms of the crickets Phalangopsidae.

FIGURES 123–142 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURES 123–142. Spectrogram of the calling song of crickets. 123–141, Sonograms of the crickets Nemobiinae; 142, Sonogram of the cricket Gryllotalpidae.

FIGURES 88–103 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURES 88–103. Spectrogram of the calling song of crickets. 88–89, 90, 92, 94, Sonograms of the crickets Eneopterinae; 91, 93, 95, Oscillograms of the crickets Eneopterinae; 96–103, Sonograms of the crickets Oecanthinae.

FIGURES 68–87 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURES 68–87. Spectrogram of the calling song of crickets. 68–83, Sonograms of the crickets Gryllinae; 84–87, Sonograms of the crickets Podoscirtinae.

FIGURES 50–67 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURES 50–67. Photograph of crickets in vivo. 50–51, Phalangopsidae; 52–66, Nemobiinae; 67, Gryllotalpidae. Photos 50, 51 by Castro-Souza, R.A.; 52, 54, 60, 61, 62, 65, 66 by Zefa, E.; 53, 55–59, 63, 64 by Martins, L.P.

FIGURES 26–49 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURES 26–49. Photograph of crickets in vivo. 26–30, Oecanthinae; 31–32, Mogoplistidae; 33–49, Phalangopsidae. Photos 26–29, 35, 43–46, 48, 49 by Zefa, E.; 30 by Fianco, M.; 31, 32, 34, 36–42 by Martins, L.P.; 33 by Cenci, R.

FIGURE 1 in Singing crickets from Brazil (Orthoptera: Gryllidea), an illustrated checklist with access to the sounds produced

FIGURE 1. Map of Brazil showing States (in colors) where crickets species/sonotypes were registered.

opencc-by-4.0Nov 2022View details →

MEMEX_Ds10d_Sounds_v1.0

Barcelona Pilot Audios/Sounds.

opencc-by-4.0Dec 2022View details →

Observations of Autumnal Cooling in a Large Estuary: The Glider Data from Long Island Sound in 2014

This data file is a component of the data used in a paper to appear in the Journal of Geophysical Research in 2023 entitled "Observations of Autumnal Cooling in a Large Estuary" by Amin Ilia, Grant McCardell, Kay Howard-Strobel, and James O'Donnell.  The data file contains the measurements from a Slocum Glider (V1) from Webb Research used in the paper. The data is in a MATLAB binary (.mat) file as a structure variable with a field MetaData containing some notes, and data in      SampleTimeEST- the date and time of the sample (EST) in MATLAB's datenum() format     Pressure_dBar - the pressure (or equivalently depth in m) that the sample was acquired      Temperature_C - the water temperature in Celcius     PracticalSalinty - the practical salinity     LatitudeDeg- the latitude of the sampling location (deg)     LongitudeDeg - the longitude of the sampling location (deg east) The paper's abstract is:  Seasonal variations in solar insolation and wind create an annual water temperature cycle that impacts circulation and biological processes. The waters of Long Island Sound (LIS) warm from March-February until August-October and then begin to cool. Ship surveys show that the vertical temperature structure becomes almost uniform during this season when the area experiences low air temperatures and high winds. However, no observations have resolved the temporal evolution of the vertical structure of temperature during these cooling periods because conditions inhibit ship operations. We report glider measurements of the vertical structure of water temperatures and salinities from October 22 to November 4, 2014, in eastern LIS. We find that 20m of water can cool at approximately 0.5 C/day during intervals of cold air and strong winds. We use the data to estimate heat content tendencies and infer surface fluxes. We also estimate the surface heat fluxes using buoy-mounted instruments and show they are consistent. The net heat flux to the atmosphere exceeds 600 W/m2 during the gilder deployment and approximately 66% of this is due to latent heat transfer. Using the buoy fluxes and products of an operation regional model, we show that the agreement with the fluxes derived from heat budget is improved by using local wind observations. In addition to confirming the extremely large cooling rates, our results demonstrate that gliders can be used in a complex region with strong tidal currents to resolve the temperature structure and heat budget during the severe weather of the cooling season.

opencc-by-4.0Nov 2022View details →

Joint Use of Far-Infrared and Mid-Infrared Observation for Sounding Retrievals: Learning from the Past for Upcoming Far-Infrared Missions

Dataset and software (Matlab) used for the analysis of synoptic weather pattern, information content and retrieval estimates

opencc-by-4.0Mar 2023View details →

Data from: Sound categorization by crocodilians

Dataset, acoustic signals and all original statistical codes used in the article "Sound categorization by crocodilians".

opencc-by-4.0Mar 2023View details →

SOUND-BASED DRONE FAULT CLASSIFICATION USING MULTI-TASK LEARNING

arxiv : https://arxiv.org/abs/2304.11708 Accepted at 29th International Congress on Sound and Vibration (ICSV29).  The drone has been used for various purposes including military applications, aerial photography, and pesticide spraying. However, the drone is vulnerable to external disturbances, and malfunction in propellers and motors can easily occur. To improve the safety of drone operations, early detection of mechanical faults should be made in real-time. In this paper, we propose a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. The dataset includes various operating conditions of drones, such as flight directions (front, back, right, left, clockwise, counter clockwise) and faults on propellers and motors. The drone sounds were then mixed with noises recorded in five different spots on the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB to 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms. We employ multitask learning (MTL) and incorporate the direction classification task as an auxiliary task to make the classifier learn more general audio features. The test over unseen data reveals that the proposed multitask model can successfully classify faults in drones and outperforms single-task models even with less training data.    please reorganize the file directory like below drone ㄴA ㄴB ㄴC   For each drone type A, B, and C have 54000*2 files. (Here, *2 means stereo channel, you can find mic1 and mic2 in subdirectory) They are divided into train, valid, and test by a 6:2:2 ratio. For each file, recording information is labeled below. {model_type}_{maneuvering_direction}_{fault}_{drone_file_index}_{background}_{background_file_index}_{SNR} model_type: A, B, C maneuvering_direction: F(Front), B(Back), R(Right), L(Left), C(Clockwise), CC(Counter-clockwise) fault: N (Normal), MF1~4 (Moter Failure), PC1~4 (Propeller Cut) -> 1~4 means each motor/propeller of the quadcopter.

FIG. 5 in Evolutionary Patterns in Sound Production across Fishes

FIG. 5. Sensitivity of ancestral-state reconstruction of soniferous fish clades to uncertainty of character states. (A) Box and whisker plot showing median and interquartile range of ancestral probabilities for different actinopterygian clades included in Table 1 related to increases or decreases in the number of soniferous fish families. (B) Variation in ancestral probabilities for each clade related to sampling uncertainty (the percentage of families within each clade with simulated uncertainty in character state).

FIG. 3 in Evolutionary Patterns in Sound Production across Fishes

FIG. 3. Probability of soniferous behavior being ancestral within major actinopterygian clades. (A) Otocephala and (B) Eupercaria. For phylogenetic trees showing the ancestral-state estimation and associated evolutionary probabilities of sound production being ancestral by stochastic character mapping, probability is represented as a gradient where blue indicates high and red is low probability of sound production; yellow is equivocal.

FIG. 2 in Evolutionary Patterns in Sound Production across Fishes

FIG. 2. Family-level phylogenetic tree of actinopterygians depicting evolution of soniferous behavior. Shown here are probabilities from ancestralstate reconstruction using stochastic character mapping. Probability is represented as a gradient, where blue indicates a high probability and red a low probability of soniferous behavior, and yellow is ~50% probability. Tree is pruned from species-level phylogeny (Rabosky et al., 2018) to familylevel here.

FIG. 1 in Evolutionary Patterns in Sound Production across Fishes

FIG. 1. Soniferous behavior mapped onto phylogenetic tree of actinopterygian families. Tree shows three different lines of evidence for soniferous behavior used here and its phylogenetic distribution. Tree is pruned from species-level phylogeny of Rabosky et al. (2018) to family-level here.

FIG. 4 in Evolutionary Patterns in Sound Production across Fishes

FIG. 4. Family-level phylogenetic tree of actinopterygians as shown in Figure 1, but in this case mapping the distribution of three categories of soniferous mechanisms for 88 families: SBV, swim bladder vibration; STR, stridulation; non-SBV, non-swim bladder vibration (see Results section for details).

opencc-by-4.0Apr 2023View details →

Ultrabroadband sound control with deep-subwavelength plasmacoustic metalayers - raw measurement data

The files in the archive relate to the frequency response data used in the graphical results presented in the paper "Ultrabroadband sound control with deep-subwavelength plasmacoustic metalayers" by Stanislav Sergeev, Romain Fleury, and Hervé Lissek. Data was recorded with the software: PULSE LabShop Fast Track Version 18.1.1.9 - 2014-01-16. File "alpha_absorption.txt" contains frequency response of absorption coefficient of active plasma metalayer tuned to 100% sound absorption (figure 3). First column - row number, second column - frequency in Hz, third column - magnitude. Files "R_04.txt", "R_07.txt", "R_09.txt" contain frequency response of complex reflection coefficient when plasma metalayer is tuned to various constant over frequency reflection coefficients (figure 4a). First column - row number, second column - frequency in Hz, third column - real part, fourth column - imaginary part. Files "R_04_0cm.txt", "R_04_05cm.txt", "R_04_10cm.txt", "R_04_15cm.txt" contain frequency response of complex reflection coefficient when plasma metalayer is tuned to constant over frequency reflection coefficient of magnitude 0.4 and different linearly changing phases (figure 4b). First column - row number, second column - frequency in Hz, third column - real part, fourth column - imaginary part. File "R_04_10cm.txt" was measured at 4 cm further plane. To be consistent with the other data, phase should be corrected by 4 cm shift.

opencc-by-4.0May 2023View details →

Mantle xenoliths used in "High P-T sound velocities of amphiboles: Implications for low-velocity anomalies in metasomatized upper mantle"

Mineral proportions in hydrous-mineral-bearing mantle xenoliths collected worldwide and their calculated sound velocities in this study.