A time‐frequency domain representation of sound energy by use of Wigner distribution

1987 ◽  
Vol 81 (S1) ◽  
pp. S96-S96
Author(s):  
Hideo Suzuki ◽  
Jun'ichi Kawaura ◽  
Takahiko Ono
Keyword(s):  
Frequency Domain ◽  
Wigner Distribution ◽  
Sound Energy ◽  
Time Frequency

Signature Features in Returned Echoes From Submerged Targets Insonified by Short, Broadband Pulses: Comparison of Experiments and Theory

1995 ◽  
Author(s):  
Torbjörn Ståhlsten ◽  
Hans C. Strifors ◽  
Guillermo C. Gaunaurd
Keyword(s):  
Experimental Data ◽  
Theoretical Prediction ◽  
Frequency Domain ◽  
Time Window ◽  
Wigner Distribution ◽  
Time Frequency ◽  
Comparison Of Experiments ◽  
Cross Terms ◽  
Sound Pulses ◽  
Broadband Sound

Abstract Backscattered echoes are studied from submerged elastic targets in the frequency domain and combined time-frequency domain when the targets are insonified by short, broadband sound pulses. The targets are either an air-filled spherical shell or various solid brass or steel spheres. The incident waveform is generated by weighting a sinusoidal signal with a Blackman time-window of a few cycles width. The spectrum is computed from each recorded set of experimental data and the result is shown to agree well with the theoretical prediction for the corresponding target and interrogating waveform. An advantage of the time-frequency approach is that target signatures can show the time evolution of the resonance features that identify each target. Experimentally obtained data are processed in the time-frequency domain using a pseudo-Wigner distribution (PWD). The associated time-window is Gaussian, and its width is adjusted to suppress the interference of cross-terms in the PWD, yet retaining the desired property of time-frequency concentrating the extracted features.

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

2021 ◽  
Vol 5 (2) ◽  
pp. 1-28
Author(s):  
Wentao Xie ◽  
Qian Zhang ◽  
Jin Zhang
Keyword(s):  
Frequency Domain ◽  
Orthogonal Frequency Division Multiplexing ◽  
Interaction Mechanism ◽  
Phase Changes ◽  
Experimental Result ◽  
Selective Fading ◽  
Time Frequency ◽  
Facial Action ◽  
Frequency Selective Fading ◽  
Frequency Selective

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

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