The Performance Analysis of Digital Filters and ANN in De-noising of Speech and Biomedical Signal

Ferdous, Humayra; Jahan, M. Sarwar; Tabassum, Fahima; Islam, Md. Imdadul

doi:10.5815/ijigsp.2023.01.06

Cited by 4 publications

(1 citation statement)

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“…Some authors proposed studying analog circuits behaving as 'black boxes' by ANNs, analyzing signals only from inputs and outputs of the circuits [14]. ANNs are also adopted to predict current signals [15], and to classify noise signals [16] by filtering them from undesired disturbances or interferences [17]. ANNs are also applied to predict the behavior of electronic circuits improving circuit design [18,19] and for high electron mobility transistors (HEMTs) amplifier design [20].…”

Section: Introductionmentioning

confidence: 99%

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Massaro

2024

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In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal voltage signal, which is a basic and fundamental signal for controlled manufacturing systems. The performed simulations take into account different noise signals changing their time-domain trend and frequency behavior to prove the possibility of predicting voltage outputs when complex signals are considered at the control circuit input, including additive disturbs and noises. The results highlight that it is possible to construct a good ANN training model by processing only the registered voltage output signals without considering the noise profile (which is typically unknown). The proposed model behaves as an electronic black box for Industry 5.0 manufacturing processes automating circuit and machine tuning procedures. By analyzing state-of-the-art ANNs, the study offers an innovative ANN-based versatile solution that is able to process various noise profiles without requiring prior knowledge of the noise characteristics.

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Section: Introductionmentioning

confidence: 99%

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Massaro

2024

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Development of Arithmetic/Logical Computational Procedures: Computer Aided Digital Filter Design with Bilinear Transformation

Zubair,

Olawale

2023

Lecture Notes on Data Engineering and Communications Technologies

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Electronic Piano Signal Spectrum Visualization and De-Noising Analysis Algorithm Optimization

Chen

2023

2023 7th International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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The Performance Analysis of Digital Filters and ANN in De-noising of Speech and Biomedical Signal

Cited by 4 publications

References 0 publications

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems

Development of Arithmetic/Logical Computational Procedures: Computer Aided Digital Filter Design with Bilinear Transformation

Electronic Piano Signal Spectrum Visualization and De-Noising Analysis Algorithm Optimization

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