Faculty Publications
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Publications by NITK Faculty
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Item Virtual sensor based Feedback Active Noise Control for neonates in NICU(IEEE Computer Society, 2013) Veena, S.; Pavithra, S.; Lokesha, H.; Narasimhan, S.V.; More, N.V.In this paper, a novel approach based on Feedback Active Noise Control with Virtual sensing has been proposed to reduce the Neonatal Intensive Care Unit (NICU) noise for infants in incubator. In NICU, the noise is due to infant monitoring medical equipments located in the vicinity of incubators. To address this, the algorithms in the literature are based on feedforward approach which requires a good reference to achieve effective attenuation. In an NICU environment, these algorithms require more than one reference microphone, which increases the complexity of the algorithm. This is eliminated by the proposed Feedback Active Noise Control approach as it generates its own reference from the error signal. The baby's ears must fall in the zone of silence (ZOS) to ensure noise reduction at its ears. But the volume of ZOS is inversely proportional to noise frequency and for frequencies above 1 KHz the ZOS is less than an inch. This constraint is overcome by the virtual sensing technique, which focuses the ZOS at baby's ears. The algorithm is evaluated with noise record from Neonatal Intensive Care Unit and it has resulted in 7dB more reduction at the baby's ears compared to existing algorithms. © 2013 IEEE.Item Feedback active noise control based on forward-backward LMS predictor(2013) Pavithra, S.; Narasimhan, S.V.In this paper, a new feedback active noise control (FBANC) system based on forward-backward error LMS (FBLMS) predictor is proposed. The misadjustment of the FBLMS predictor is about half that of the forward error LMS (FLMS) predictor. The new ANC system employs FBLMS predictors both for its main path (MP) predictor and for the noise canceler (NC) for the secondary path (SP) identification (SPI). To realize the MP predictor based on the FBLMS concept, a new FXLMS structure is proposed. But for the NC for the SPI, the FBLMS predictor is directly used. The MP predictor based on FBLMS reduces its misadjustment. Further the use of FBLMS predictor for the NC, as it gives a good prediction of primary noise component in the error (residual noise), improves the SNR for SPI. Thus, the improved SP estimate and the reduced misadjustment for the MP predictor achieved result in a significantly better overall noise reduction (of about 8 dB) over the ANC that uses the MP predictor and noise canceler for SPI, both based only on the forward error LMS algorithm. The computational load for the proposed algorithm is about twice that of FBANC that uses only forward error. © 2012 Springer-Verlag London Limited.Item Feedback active noise control based on transform-domain forward-backward LMS predictor(Springer London, 2014) Pavithra, S.; Narasimhan, S.V.In this paper, a new feedback active noise control (FBANC) system based on the transform-domain forward-backward LMS (TFBLMS) predictor has been proposed. The new ANC system employs the TFBLMS predictor for its main-path (MP) predictor as well as for the noise canceller. To overcome the ill effect of the primary noise field, which acts as an observation noise for the secondary-path (SP) identification, the noise canceller is used. As the main-path predictor is based on the TFBLMS, its convergence rate improves due to its input orthogonalization. Further, its FBLMS nature reduces misadjustment. The use of TFBLMS predictor for noise canceller also gives a good prediction of primary noise at a faster rate, enabling improved SP identification. This improved SP identification indirectly aids the MP predictor to achieve an improved performance. A new filtered-x LMS structure has been proposed to realize the new MP predictor to accommodate the TFBLMS algorithm. The TFBLMS algorithm is applied directly to the noise canceller for SP identification. The proposed new ANC system has been found to have a significantly better noise reduction (by 14.6 dB) over the FBANC system based on tapped delay line time-domain FBLMS algorithm. © 2012 Springer-Verlag London Limited.