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Author: search.filters.author.Goel, Pankaj

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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 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.