Comparative performance analysis and hardware implementation of adaptive filter algorithms for acoustic noise cancellation

Dewasthale, Mugdha M.; Kharadkar, R. D.; Bari, Mrunali

doi:10.1109/infop.2015.7489363

Cited by 5 publications

(5 citation statements)

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“…The least mean square (LMS) algorithm is a commonly used adaptive filtering algorithm [21]. It carries out automatic adjustment of the current filter parameters according to the estimation of the filter parameters at the previous moment to adapt to the statistical characteristics of signal and noise changes, thus realizing optimal filtering.…”

Section: Proposed Ffplms Features 1) Least Mean Squarementioning

confidence: 99%

Fusion Feature Extraction Based on Auditory and Energy for Noise-Robust Speech Recognition

et al. 2019

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Environmental noise can pose a threat to the stable operation of current speech recognition systems. It is therefore essential to develop a front feature set that is able to identify speech under low signalto-noise ratio. In this paper, a robust fusion feature is proposed that can fully characterize speech information. To obtain the cochlear filter cepstral coefficients (CFCC), a novel feature is first extracted by the power-law nonlinear function, which can simulate the auditory characteristics of the human ear. Speech enhancement technology is then introduced into the front end of feature extraction, and the extracted feature and their first-order difference are combined in new mixed features. An energy feature Teager energy operator cepstral coefficient (TEOCC) is also extracted, and combined with the above-mentioned mixed features to form the fusion feature sets. Principal component analysis (PCA) is then applied to feature selection and optimization of the feature set, and the final feature set is used in a non-specific persons, isolated words, and smallvocabulary speech recognition system. Finally, a comparative experiment of speech recognition is designed to verify the advantages of the proposed feature set using a support vector machine (SVM). The experimental results show that the proposed feature set not only display a high recognition rate and excellent anti-noise performance in speech recognition, but can also fully characterize the auditory and energy information in the speech signals.INDEX TERMS Cochlear filter cepstral coefficients, Teager energy operators cepstral coefficients, principal component analysis, speech recognition.

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Section: Proposed Ffplms Features 1) Least Mean Squarementioning

confidence: 99%

Fusion Feature Extraction Based on Auditory and Energy for Noise-Robust Speech Recognition

et al. 2019

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“…and gradient noise amplification [65] • HOT based LMS adaptive filter is computationally effective and has fast convergence as compared to…”

Section: Deep Neural Network For Speech Enhancementmentioning

confidence: 99%

A Survey on Techniques for Enhancing Speech

Tayseer¹,

Adeel²,

Hussain³

2018

IJCA

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Speech enhancement is used in almost all the modern communication systems. It is obvious that when speech is being transmitted, its quality may degrade due to interference in the environment it is passing through. Some of the interferences that may affect the speech quality of transit include acoustic additive noise, acoustic reverberation or white Gaussian noise. This paper focuses on the techniques that appeared in the literature to enhance the signal of speech. Various methods used include wiener filter, statistical methods, subspace method, basic spectral subtraction method and spectral subtraction. In this paper authors will discuss various such methods along with their advantages and disadvantages.

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“…The main problem solved by ANC systems is the interference of external noise that affects the signals of interest, a problem presented in multi-sensor networks in new smart cities [6]. This is done using adaptive algorithms that control the generation of the desired signal by adjusting the filter weights to minimize error as much as possible [7].…”

Section: Introductionmentioning

confidence: 99%

“…RLS filters have a high rate of convergence that is independent of the dispersion of the eigenvalues of the input correlation matrix. These algorithms are very useful in applications where ambient noise varies slowly [7]. Some research has made comparisons between adaptive algorithms for acoustic noise cancellation [7].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Noise Cancellation System Using a Recursive Least Squares Filter

2020

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In this paper, we show an Adaptive Noise Canceller (ANC) that estimate an original audio a signal measured with noise. Adaptive system is implemented using a Recursive Least Squares filter (RLS). Its design parameters consider the filter order, forgetting factor and initial conditions to obtain optimal coefficients through iterations. A medium square error (MSE) around to 10-6 is reached, and with this it makes possible a low-cost implementation.

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Comparative performance analysis and hardware implementation of adaptive filter algorithms for acoustic noise cancellation

Cited by 5 publications

References 1 publication

Fusion Feature Extraction Based on Auditory and Energy for Noise-Robust Speech Recognition

Fusion Feature Extraction Based on Auditory and Energy for Noise-Robust Speech Recognition

A Survey on Techniques for Enhancing Speech

Adaptive Noise Cancellation System Using a Recursive Least Squares Filter

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