scholarly journals A tutorial example of duct acoustics mode detections with machine-learning-based compressive sensing

2019 ◽  
Vol 146 (4) ◽  
pp. EL342-EL346 ◽  
Author(s):  
Xun Huang
Keyword(s):  
Machine Learning ◽  
Compressive Sensing ◽  
Duct Acoustics

Machine learning improvements to compressive sensing for speech enhancement in hearing aid applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hrishikesh B Vanjari ◽  
Mahesh T Kolte
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Hearing Loss ◽  
Compressive Sensing ◽  
Speech Enhancement ◽  
Hearing Aid ◽  
World Health ◽  
Content Type ◽  
L2 Norm ◽  
Reconstructed Signal

Purpose Speech is the primary means of communication for humans. A proper functioning auditory system is needed for accurate cognition of speech. Compressed sensing (CS) is a method for simultaneous compression and sampling of a given signal. It is a novel method increasingly being used in many speech processing applications. The paper aims to use Compressive sensing algorithm for hearing aid applications to reduce surrounding noise. Design/methodology/approach In this work, the authors propose a machine learning algorithm for improving the performance of compressive sensing using a neural network. Findings The proposed solution is able to reduce the signal reconstruction time by about 21.62% and root mean square error of 43% compared to default L2 norm minimization used in CS reconstruction. This work proposes an adaptive neural network–based algorithm to enhance the compressive sensing so that it is able to reconstruct the signal in a comparatively lower time and with minimal distortion to the quality. Research limitations/implications The use of compressive sensing for speech enhancement in a hearing aid is limited due to the delay in the reconstruction of the signal. Practical implications In many digital applications, the acquired raw signals are compressed to achieve smaller size so that it becomes effective for storage and transmission. In this process, even unnecessary signals are acquired and compressed leading to inefficiency. Social implications Hearing loss is the most common sensory deficit in humans today. Worldwide, it is the second leading cause for “Years lived with Disability” the first being depression. A recent study by World health organization estimates nearly 450 million people in the world had been disabled by hearing loss, and the prevalence of hearing impairment in India is around 6.3% (63 million people suffering from significant auditory loss). Originality/value The objective is to reduce the time taken for CS reconstruction with minimal degradation to the reconstructed signal. Also, the solution must be adaptive to different characteristics of the signal and in presence of different types of noises.

scholarly journals Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach

2019 ◽  
Vol 19 (1) ◽  
pp. 293-304 ◽  
Author(s):  
Yuequan Bao ◽  
Zhiyi Tang ◽  
Hui Li
Keyword(s):  
Machine Learning ◽  
Structural Health Monitoring ◽  
Compressive Sensing ◽  
Health Monitoring ◽  
Applied Mathematics ◽  
Modal Identification ◽  
Data Reconstruction ◽  
Wireless Data ◽  
Sensing Data ◽  
Structural Health

Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification. The key issue in compressive sensing is finding the optimal solution for sparse optimization. In the past several years, many algorithms have been proposed in the field of applied mathematics. In this article, we propose a machine learning–based approach to solve the compressive-sensing data-reconstruction problem. By treating a computation process as a data flow, the solving process of compressive sensing–based data reconstruction is formalized into a standard supervised-learning task. The prior knowledge, i.e. the basis matrix and the compressive sensing–sampled signals, is used as the input and the target of the network; the basis coefficient matrix is embedded as the parameters of a certain layer; and the objective function of conventional compressive sensing is set as the loss function of the network. Regularized by l1-norm, these basis coefficients are optimized to reduce the error between the original compressive sensing–sampled signals and the masked reconstructed signals with a common optimization algorithm. In addition, the proposed network is able to handle complex bases, such as a Fourier basis. Benefiting from the nature of a multi-neuron layer, multiple signal channels can be reconstructed simultaneously. Meanwhile, the disassembled use of a large-scale basis makes the method memory-efficient. A numerical example of multiple sinusoidal waves and an example of field-test wireless data from a suspension bridge are carried out to illustrate the data-reconstruction ability of the proposed approach. The results show that high reconstruction accuracy can be obtained by the machine learning–based approach. In addition, the parameters of the network have clear meanings; the inference of the mapping between input and output is fully transparent, making the compressive-sensing data-reconstruction neural network interpretable.

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