acoustic signal
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2022 ◽  
Vol 185 ◽  
pp. 111778
Author(s):  
Mohammad Hosseinpour-Zarnaq ◽  
Mahmoud Omid ◽  
Amin Taheri-Garavand ◽  
Amin Nasiri ◽  
Asghar Mahmoudi

2022 ◽  
Vol 3 (2) ◽  
pp. 1-22
Author(s):  
Ye Gao ◽  
Asif Salekin ◽  
Kristina Gordon ◽  
Karen Rose ◽  
Hongning Wang ◽  
...  

The rapid development of machine learning on acoustic signal processing has resulted in many solutions for detecting emotions from speech. Early works were developed for clean and acted speech and for a fixed set of emotions. Importantly, the datasets and solutions assumed that a person only exhibited one of these emotions. More recent work has continually been adding realism to emotion detection by considering issues such as reverberation, de-amplification, and background noise, but often considering one dataset at a time, and also assuming all emotions are accounted for in the model. We significantly improve realistic considerations for emotion detection by (i) more comprehensively assessing different situations by combining the five common publicly available datasets as one and enhancing the new dataset with data augmentation that considers reverberation and de-amplification, (ii) incorporating 11 typical home noises into the acoustics, and (iii) considering that in real situations a person may be exhibiting many emotions that are not currently of interest and they should not have to fit into a pre-fixed category nor be improperly labeled. Our novel solution combines CNN with out-of-data distribution detection. Our solution increases the situations where emotions can be effectively detected and outperforms a state-of-the-art baseline.


Author(s):  
Roberto Outa ◽  
Fabio Roberto Chavarette ◽  
Vishnu Narayan Mishra ◽  
Aparecido Carlos Gonçalves ◽  
Adriana Garcia ◽  
...  

This work is of multidisciplinary concept, whose development is difficult to perform. Considering also that, in one of the steps, the similarity between the FRF of the vibration and acoustic signal is demonstrated. The objective of this work is the analysis and prognosis of the progression of failures of a pair of gears using the artificial immune system (AIS) of negative selection. In order to have this condition met, during the development of this work, the Wiener filter technique, the vibration and acoustic signal analysis (FRF), the application of negative selection AIS techniques for classification and grouping of signals were applied. The final result successfully demonstrates the effectiveness of the development process of this work and the robustness of the negative selection AIS algorithm.


2022 ◽  
Author(s):  
Shohei Sakaida ◽  
Iuliia Pakhotina ◽  
Ding Zhu ◽  
A. D. Hill

Abstract Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) measurements during hydraulic fracturing treatments are used to estimate fluid volume distribution among perforation clusters. DAS is sensitive to the acoustic signal induced by fluid flow in the near-well region during pumping a stage, while DTS is sensitive to temperature variation caused by fluid flow inside the wellbore and in the reservoir. Raw acoustic signal has to be transferred to frequency band energy (FBE) which is defined as the integration of the squared raw measurements in each DAS channel location for a fixed period of time. In order to be used in further interpretation, FBE has to be averaged between several fiber-optic channels for each cluster on each time step. Based on this input, DAS allows us to consider fluid flow through perforation stage by stage during an injection period, and to evaluate the volume of fluid pumped in each cluster location as a function of time, and therefore to estimate the cumulative volume of fluid injected into each cluster. This procedure is based on a lab-derived and computational dynamics model confirmed correlation between the acoustic signal and the flow rate. At each time step, we apply the perforation/fracture noise correlation to determine the flow rate into each cluster, constrained by the requirement that the sum of the flow rates into individual clusters must equal the total injection rate at that time. On the other hand, the DTS interpretation method is based on the transient temperature behavior during the fracturing stimulation. During injection, the temperature of the reservoir surrounding the well is cooled by the injection fluid inside the well. After shut-in of stage pumping, temperature recovers at a rate depending on the injected volume of fluid at the location. The interpretation procedure is based on the temperature behavior during the warm-back period. This temperature distribution is obtained by solution of a coupled 3-D reservoir thermal model with 1-D wellbore thermal model iteratively. Once we confirm that the DAS and DTS interpretation methods provide comparable results of the fluid volume distribution, either of the interpretation results can be used as a known input parameter for the other interpretation method to estimate additional unknown such as one of the fracture properties. In this work, the injected fluid volume distribution obtained by the DAS interpretation is used as an input parameter for a forward model which computes the temperature profile in the reservoir. By conducting temperature inversion to reproduce the temperature profile that matches the measured temperature with the fixed injection rate for each cluster, we can predict distribution of injected fluid for hydraulic fractures along a wellbore. The temperature inversion shows that multiple fractures are created in a swarm pattern from each perforation cluster with a much tighter spacing than the cluster spacing. The field data from MIP-3H provided by the Marcellus Shale Energy and Environmental Laboratory is used to demonstrate the DAS/DTS integrated interpretation method. This approach can be a valuable means to evaluate the fracturing treatment design and further understand the field observation of hydraulic fractures.


2022 ◽  
Author(s):  
Wei-Wei Kan ◽  
Qiu-Yu Li ◽  
Lei Pan

Abstract The scattering behavior of the anisotropic acoustic medium is analyzed to reveal the possibility of routing acoustic signals through the anisotropic layers with no backscattering loss. The sound-transparent effect of such medium is achieved by independently modulating the anisotropic effective acoustic parameters in a specific order, and experimentally observed in a bending waveguide by arranging the subwavelength structures in the bending part according to transformation acoustics. With the properly designed filling structures, the original distorted acoustic field in the bending waveguide is restored as if the wave travels along a straight path. The transmitted acoustic signal is maintained nearly the same as the incident modulated Gaussian pulse. The proposed schemes and the supporting results could be instructive for further acoustic manipulations such as wave steering, cloaking and beam splitting.


2021 ◽  
pp. 026765832110664
Author(s):  
John Archibald

In this research note I want to address some misunderstandings about the construct of redeployment and suggest that we need to fit these behavioural data from Yang, Chen and Xiao (YCX) into a broader context. I will suggest that these authors’ work is not just about the failure of three models to predict equivalence classification. Equivalence classification is not the end of the story but only the beginning. We need to look at what cues are detected in the input, which subset of the input becomes intake, and how this intake is parsed onto phonological structures. The empirical results of YCX should not be viewed as some sort of non-result inasmuch as none of the proposed predictors of Mandarin equivalence classification foresaw that the Russian prevoiced stops and short-lag stops would be equated with the Mandarin short-lag stops. Rather, the empirical results need to be contextualized by considering such factors as cue reweighting as part of the learning theory which maps intake onto phonological representations. In this light, the results are not a repudiation of phonological redeployment, but help to shed light on the parsing of the acoustic signal, the importance of robust burst-release cues, and the non-local nature of L2 phonological learning (as opposed to noticing).


Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
Jenessa Duncombe

Hundreds of volcanic explosions detected underwater at KīlaueaThe explosions, identified during the 2018 eruption phase, offer a clear acoustic signal that researchers could use to measure ocean properties.


AIP Advances ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 125228
Author(s):  
Liang Qiao ◽  
Xiaobing Zhang ◽  
Kai Ding ◽  
Zhen Han ◽  
Bing Yan ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Baptiste Chide ◽  
Olivier Beyssac ◽  
Michel Gauthier ◽  
Karim Benzerara ◽  
Imène Estève ◽  
...  

AbstractThe SuperCam instrument suite onboard the Mars 2020 Perseverance rover uses the laser-induced breakdown spectroscopy (LIBS) technique to determine the elemental composition of rocks and soils of the Mars surface. It is associated with a microphone to retrieve the physical properties of the ablated targets when listening to the laser-induced acoustic signal. In this study, we report the monitoring of laser-induced mineral phase transitions in acoustic data. Sound data recorded during the laser ablation of hematite, goethite and diamond showed a sharp increase of the acoustic signal amplitude over the first laser shots. Analyses of the laser-induced craters with Raman spectroscopy and scanning electron microscopy indicate that both hematite and goethite have been transformed into magnetite and that diamond has been transformed into amorphous-like carbon over the first laser shots. It is shown that these transitions are the root cause of the increase in acoustic signal, likely due to a change in target’s physical properties as the material is transformed. These results give insights into the influence of the target’s optical and thermal properties over the acoustic signal. But most importantly, in the context of the Mars surface exploration with SuperCam, as this behavior occurs only for specific phases, it demonstrates that the microphone data may help discriminating mineral phases whereas LIBS data only have limited capabilities.


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