scholarly journals Compensatory mechanisms affect sensorimotor integration during ongoing vocal-motor acts in marmoset monkeys

2019 ◽  
Author(s):  
Thomas Pomberger ◽  
Julia Löschner ◽  
Steffen R. Hage

AbstractIn vertebrates, any transmission of vocal signals faces the challenge of acoustic interferences such as heavy rain, wind, animal, or urban sounds. Consequently, several mechanisms and strategies have evolved to optimize the signal-to-noise ratio. Examples to increase detectability are the Lombard effect, an involuntary rise in call amplitude in response to masking ambient noise, which is often associated with several other vocal changes such as call frequency and duration, as well as the animals’ capability of limiting calling to periods where noise perturbation is absent. Previous studies revealed rapid vocal flexibility and various audio-vocal integration mechanisms in marmoset monkeys. Using acoustic perturbation triggered by vocal behavior, we investigated whether marmoset monkeys are capable of exhibiting changes in call structure when perturbing noise starts after call onset or whether such effects only occur if noise perturbation starts prior to call onset. We show that marmoset monkeys are capable of rapidly modulating call amplitude and frequency in response to such perturbing noise bursts. Vocalizations swiftly increased call frequency after noise onset indicating a rapid effect of perturbing noise on vocal motor pattern production. Call amplitudes were also affected. Interestingly, however, the marmosets did not exhibit the Lombard effect as previously reported but decreased their call intensity in response to perturbing noise. Our findings indicate that marmosets possess a general avoidance strategy to call in the presences of ambient noise and suggest that these animals are capable of counteracting a previously thought involuntary audio-vocal mechanism, the Lombard effect, presumably via cognitive control processes.

1981 ◽  
Vol 229 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Brian Mulloney ◽  
Donald H. Perkel ◽  
Rubén W. Budelli

2020 ◽  
Vol 223 (19) ◽  
pp. jeb225284
Author(s):  
Manman Lu ◽  
Guimin Zhang ◽  
Jinhong Luo

ABSTRACTFlexible vocal production control enables sound communication in both favorable and unfavorable conditions. The Lombard effect, which describes a rise in call amplitude with increasing ambient noise, is a widely exploited strategy by vertebrates to cope with interfering noise. In humans, the Lombard effect influences the lexical stress through differential amplitude modulation at a sub-call syllable level, which so far has not been documented in animals. Here, we bridge this knowledge gap with two species of Hipposideros bats, which produce echolocation calls consisting of two functionally well-defined units: the constant-frequency (CF) and frequency-modulated (FM) components. We show that ambient noise induced a strong, but differential, Lombard effect in the CF and FM components of the echolocation calls. We further report that the differential amplitude compensation occurred only in the spectrally overlapping noise conditions, suggesting a functional role in releasing masking. Lastly, we show that both species of bats exhibited a robust Lombard effect in the spectrally non-overlapping noise conditions, which contrasts sharply with the existing evidence. Our data highlight echolocating bats as a potential mammalian model for understanding vocal production control.


2014 ◽  
Vol 12 (1) ◽  
pp. 29-38
Author(s):  
Silvanus Teneng Kiyang ◽  
Robert Van Zyl

Purpose – The purpose of this work is to assess the influence of ambient noise on the performance of wireless sensor networks (WSNs) empirically and, based on these findings, develop a mathematical tool to assist technicians to determine the maximum inter-node separation before deploying a new WSN. Design/methodology/approach – A WSN test platform is set up in an electromagnetically shielded environment (RF chamber) to accurately control and quantify the ambient noise level. The test platform is subsequently placed in an operational laboratory to record network performance in typical unshielded spaces. Results from the RF chamber and the real-life environments are analysed. Findings – A minimum signal-to-noise ratio (SNR) at which the network still functions was found to be of the order 30 dB. In the real-life scenarios (machines, telecommunications and computer laboratories), the measured SNR exceeded this minimum value by more than 20 dB. This is due to the low ambient industrial noise levels observed in the 2.4 GHz ISM band for typical environments found at academic institutions. It, therefore, suggests that WSNs are less prone to industrial interferences than anticipated. Originality/value – A predictive mathematical tool is developed that can be used by technicians to determine the maximum inter-node separation before the WSN is deployed. The tool yields reliable results and promises to save installation time.


2019 ◽  
Vol 109 (5) ◽  
pp. 1716-1728
Author(s):  
Rhys Hawkins ◽  
Malcolm Sambridge

Abstract A method of extracting group and phase velocity dispersions jointly for Love‐ and Rayleigh‐wave observations is presented. This method uses a spectral element representation of a path average Earth model parameterized with density, shear‐wave velocity, radial anisotropy, and VP/VS ratio. An initial dispersion curve is automatically estimated using a heuristic approach to prevent misidentification of the phase. A second step then more accurately fits the observed noise correlation function (NCF) between interstation pairs in the frequency domain. For good quality cross correlations with reasonable signal‐to‐noise ratio, we are able to very accurately fit the spectrum of NCFs and hence obtain reliable estimates of both phase and group velocity jointly for Love and Rayleigh surface waves. In addition, we also show how uncertainties can be estimated with linearized approximations from the Jacobians and subsequently used in tomographic inversions.


2014 ◽  
Vol 13 (03) ◽  
pp. 1450018
Author(s):  
S. Sakthivel Murugan ◽  
V. Natarajan ◽  
S. Prethivika

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.


2020 ◽  
Vol 148 (4) ◽  
pp. 2752-2752
Author(s):  
Rachael N. Piper ◽  
Brianna Legner ◽  
Alyse Ruda ◽  
PASQUALE BOTTALICO

2021 ◽  
Author(s):  
Zhenyu Jin

Object. Electroencephalography (EEG) signals suffer from a low signal-to-noise ratio and are very susceptible to muscular, ambient noise, and other artifacts. Many artifact removal algorithms have been proposed to address this problem. However, the evaluation of these algorithms is conventionally too indirect (e.g., black-box comparisons of brain-computer interface performance before and after removal) because it is unclear which part of the signal represents raw EEG and which is noise. This project objectively benchmarks popular artifact removal algorithms and evaluates the fundamental Independent Component Analysis (ICA) approach thanks to a unique dataset where EEG is recorded simultaneously with other physiological signals-facial electromyography (EMG), accelerometers, and gyroscope-while ten subjects perform several repetitions of common artifact-inflicting tasks (blinking, speaking, etc.). Approach. I have compared the correlation between EEG signals and the artifact-representing channels before and after applying an artifact removal algorithm across the different artifact-inflicting tasks. The extent to which an artifact removal method can reduce this correlation objectively quantifies its effectiveness for the different artifacts. In the same direction, I have determined to what extent ICA successfully detects artefactual components in EEG by comparing the corresponding correlations for independent components that are labeled as artifacts with those labeled as EEG. Main result. The FORCe was found to be the most effective and generic artifact removal method, cleaning almost 40% of artifacts. ICA is shown to be able to isolate almost 70% of artefactual components. Significance. This work alleviates the problem of unreliable evaluation of EEG artifact removal frameworks and provides the first reliable benchmark for the most popular algorithms in this literature.


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