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    Improved Active Noise Cancellation Using Variable Step-Size Combined Fx-LMS Algorithm
    (2025-01-01)
    Kar, Asutosh
    ;
    Burra, Srikanth
    ;
    Shoba, S.
    ;
    Vasundhara
    ;
    Mladenovic, Vladimir
    Active noise cancellation of audio signals, which has been the subject of a substantial quantity of research in the past, remains a significant obstacle in many aspects of acoustic signal processing. In a natural environment, the system’s parameters are in constant flux. Consequently, we shall investigate the filtered-x least mean square (FxLMS) algorithm. The adaptive filter is used as a controller to generate an anti-noise signal in the feedforward FxLMS algorithm, which only addressed narrowband and broadband noise. The feedback FxLMS algorithm, on the other hand, enhances the algorithm’s convergence, but cannot eliminate the effect of residual noise’s uncorrelated disturbance. However, both the correlated and uncorrelated disturbances must be under user control. Therefore, a combination of the feedback and feed-forward FxLMS algorithms was employed, which consists of two portions for estimating the primary disturbances and the uncorrelated disturbances. A variable step size algorithm has been employed to attain a balance between system stability and control efficiency. In Akhtar’s method, an external adaptive filter was applied to the feed-forward system in order to estimate a more precise error signal and enhance the performance of the systems. These improvements will improve the algorithm’s ability to control both primary and uncorrelated noise.
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    Adaptive Tap-Length Based Sub-band Mean M-Estimate Filtering for Active Noise Cancellation
    (2024-09-01)
    Kar, Asutosh
    ;
    Shoba, S.
    ;
    Burra, Srikanth
    ;
    Goel, Pankaj
    ;
    Kumar, Sanjeev
    Electronic equipment used on a daily basis now frequently includes active noise cancellation. The adaptive filters, which are positioned within, are essential for noise cancellation. An essential component to take into account for the overall performance is the structural and computational complexity of the filter. The filter’s structure has an impact on this. The amount of taps determines the structure. Active noise cancellation filters often have set tap lengths and are lengthy, which causes sluggish convergence and delay. As a result, a trade-off between the filter’s length and convergence is required. This is conceivable if there is a flexible filter with a tap length that adapts to the environment while still ensuring acceptable convergence. This study proposes a novel Minimum Mean M-estimate method with changeable tap length and uses a sub-band adaptive filtering technique to shorten the filter’s length. In order to maximize the filter’s efficiency, the advantages of three approaches are specifically merged in this work. They are the proposed algorithm, the proposed method’s variable tap length variant, and the sub-band adaptive filtering. The simulation’s findings and recommendations are supported.
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    A deep neural network-correlation phase sensitive mask based estimation to improve speech intelligibility
    (2023-09-01)
    Sivapatham, Shoba
    ;
    Kar, Asutosh
    ;
    Bodile, Roshan
    ;
    Mladenovic, Vladimir
    ;
    Sooraksa, Pitikhate
    General masking-based speech enhancement using a deep learning architecture (DNN) approach focuses on the spectral values of the speech in order to show improvement in intelligibility. But, the residual noise present in the phase spectrum and inter-channel correlation dependency between noise and noisy speech can impact the results of intelligibility in speech enhancement. This research work proposes a correlation phase-sensitive novel mask which contains phase, magnitude spectral and inter-channel correlation for a tangible improvement in speech intelligibility. The correlation parameter finds the dependency between the signals and phase spectrum factor eliminates the residual noise. In addition, selecting the prior features from the feature combination also helps in reducing the dimensionality and increases the accuracy of the enhanced speech. This work also aims to decrease the complexity of the DNN by analysing the network with different parameters. The performance of the mask is evaluated with various intelligibility factors. The proposed mask has been compared with different mask estimators. The proposed mask has generated estimated speech with an increase in the intelligibility of 0.02-0.001 over six different noises and four different signal-to-noise (SNR) levels.
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    A Family of Adaptive Volterra Filters Based on Maximum Correntropy Criterion for Improved Active Control of Impulsive Noise
    (2022-02-01)
    Gowtham, Guttikonda
    ;
    Burra, Srikanth
    ;
    Kar, Asutosh
    ;
    Østergaard, Jan
    ;
    Sooraksa, Pitikhate
    Linear active noise control (ANC) systems have been used in the past to effectively suppress Gaussian noise. A practical ANC system must consider nonlinearities in the secondary path with a non-minimum phase. For an ANC system to effectively operate in a modern-day acoustic environment with multiple electrical/electronic systems operating in the vicinity, the reference noise source is taken as a non-Gaussian stochastic process. Linear systems have shown unacceptable performance in countering the disturbances that are impulsive in nature. Considering these issues, we propose a family of adaptive algorithms for ANC systems that employ a second-order Volterra filter for accurate modeling of the impulsive disturbances. We utilize the maximum correntropy criterion as the cost function to improve the adaptive filtering process, which generates the most appropriate output signal in the ANC system’s successive iterative stages. The proposed algorithms feature dynamic learning-rate parameters, which improve the tracking performance of the algorithms. Also, a careful selection of the Volterra filter’s kernel size in the proposed algorithms ensures a balance between system stability and convergence rate. These parameters are made automatically adjustable, based on the residual error and the reference input signals, to optimize performance in non-Gaussian environments. A comparison of the proposed algorithms with its counterparts is presented through computer simulations in terms of average noise reduction for different levels of impulsive noise. The proposed algorithms are also compared with each other to find the best performing algorithm in a non-Gaussian environment. Achieved results exhibit the effectiveness of the proposed algorithms in their ability to attenuate impulsive noise in comparison with the existing adaptive algorithms for ANC systems. Later, the proposed algorithm has been implemented for speech enhancement, where the noise from the speech sample is identified and removed using an adaptive filter algorithm. Simulations reveal that the devised algorithm can effectively denoise speech signals. Further, in the last part of the paper, the VF-MCCRMC is modified in accordance with the energy of the error signal, and the simulation results demonstrated the improvement in stability and error performance.
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    A DNN-Based Accurate Masking Using Significant Feature Sets
    (2022-01-01)
    Sivapatham, Shoba
    ;
    Goel, Pankaj
    ;
    Burra, Srikanth
    ;
    Sooraksa, Pitikhate
    ;
    Kar, Asutosh
    Monaural speech separation has remained a very challenging problem for a longtime which can be addressed using a supervised learning approach that uses features of the noisy input to predict an accurate time-frequency mask. Effective acoustic phonetic features can help in the accurate mask prediction at low Signal-to-Noise Ratios (SNRs). Individual features capture specific attributes of the audio signal; therefore, it's essential to employ a set of features. This work examines different combinations of monaural features as input and ideal ratio mask a straining target to the DNN model. Feature combination sets are constructed by examining single features and then combining the most relevant ones. The results are evaluated for different feature combinations under non-stationary noises at low SNR levels. The feature performance is evaluated by using intelligibility and quality measures. A combination of two features is considered the best feature combination as it indicates a significant increase in speech intelligibility as compared to individual features and combinations consisting of more than two features.