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5 results for “acoustic signal processing”
Sex-specific speed-accuracy tradeoffs shape neural processing of acoustic signals in a grasshopper
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Acoustic Emission Dataset for Multi-Laser LPBF Systems: Supporting DUAL DISCO Signal Processing Technique
<p>This dataset contains acoustic emission (AE) signals collected during experiments with multi-laser Laser Powder Bed Fusion (LPBF) systems. The data supports the research presented in the paper titled "DUAL DISCO: A Novel Approach to Acoustic Emission Monitoring in Multi-Laser LPBF Systems." The dataset is designed to facilitate the development and validation of advanced signal processing techniques, specifically the DUAL DISCO method, which aims to disentangle and analyze AE signals from simultaneous laser operations.</p> <p><strong>Contents:</strong></p> <ul> <li> <p><strong>Raw_data.zip:</strong> This file contains the AE signals used for training. The data was recorded using two condenser microphones positioned around the LPBF build plate, capturing signals from both sequential and simultaneous laser operations across various melting regimes (conduction and keyhole modes).</p> </li> <li> <p><strong>Raw_data_test.zip:</strong> This file includes the AE signals used for testing, recorded under different experimental conditions to evaluate the generalization capabilities of signal processing algorithms.</p> </li> <li> <p><strong>params.xlsx:</strong> This spreadsheet provides the ground truth labels for the training data, indicating the melting regime (conduction or keyhole) for each signal in Raw_data. </p> </li> <li> <p><strong>params_test.xlsx:</strong> This spreadsheet contains the ground truth labels for the test data, similarly indicating the melting regime for each signal in Raw_data_test.</p> </li> </ul> <p><strong>Applications:</strong></p> <p>This dataset is intended for researchers and practitioners in the field of additive manufacturing and signal processing. It can be used to:</p> <ul> <li>Develop and test new algorithms for AE signal processing in multi-laser LPBF systems.</li> <li>Explore the acoustic characteristics of different melting regimes.</li> <li>Enhance the understanding of process monitoring techniques in additive manufacturing.</li> </ul> <p><strong>Acknowledgments:</strong></p> <p>The dataset was collected using the AddUp FormUp 350 machine and is part of a research project supported by the Bern Economic Development Agency. Special thanks to Thomas Rytz for technical support.</p>
Data from: Processing of simple and complex acoustic signals in a tonotopically organized ear
Processing of complex signals in the hearing organ remains poorly understood. This paper aims to contribute to this topic by presenting investigations on the mechanical and neuronal response of the hearing organ of the tropical bushcricket species Mecopoda elongata to simple pure tone signals as well as to the conspecific song as a complex acoustic signal. The high-frequency hearing organ of bushcrickets, the crista acustica (CA), is tonotopically tuned to frequencies between about 4 and 70 kHz. Laser Doppler vibrometer measurements revealed a strong and dominant low-frequency-induced motion of the CA when stimulated with either pure tone or complex stimuli. Consequently, the high-frequency distal area of the CA is more strongly deflected by low-frequency-induced waves than by high-frequency-induced waves. This low-frequency dominance will have strong effects on the processing of complex signals. Therefore, we additionally studied the neuronal response of the CA to native and frequency-manipulated chirps. Again, we found a dominant influence of low-frequency components within the conspecific song, indicating that the mechanical vibration pattern highly determines the neuronal response of the sensory cells. Thus, we conclude that the encoding of communication signals is modulated by ear mechanics.
Data from: Processing of simple and complex acoustic signals in a tonotopically organized ear
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Sex-specific speed-accuracy tradeoffs shape neural processing of acoustic signals
<p>Speed-accuracy tradeoffs – being fast at the risk of being wrong – are fundamental to many decisions and natural selection is expected to resolve these tradeoffs according to the costs and benefits of behavior. We here test the prediction that females and males should integrate information from courtship signals differently because they experience different payoffs along the speed-accuracy continuum. We fitted a neural model of decision making (a drift-diffusion model of integration to threshold) to behavioral data from the grasshopper <i>Chorthippus biguttulus</i> to determine the parameters of temporal integration of acoustic directional information used by male grasshoppers to locate receptive females. The model revealed that males had a low threshold for initiating a turning response, yet a large integration time constant enabled them to continue to gather information when cues were weak. This contrasts with parameters estimated for females of the same species when evaluating potential mates, in which response thresholds were much higher and behavior was strongly influenced by unattractive stimuli. Our results reveal differences in neural integration consistent with the sex-specific costs of mate search: Males often face competition and need to be fast. Females often pay high error costs and need to be deliberate.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.