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1. A Generalized Proportionate-Type Normalized Subband Adaptive Filter

   https://doi.org/10.1109/IEEECONF44664.2019.9048906  

   Chen, Kuan-Lin; Lee, Ching-Hua; Rao, Bhaskar D.; Garudadri, Harinath (November 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers)

   We show that a new design criterion, i.e., the least squares on subband errors regularized by a weighted norm, can be used to generalize the proportionate-type normalized subband adaptive filtering (PtNSAF) framework. The new criterion directly penalizes subband errors and includes a sparsity penalty term which is minimized using the damped regularized Newton’s method. The impact of the proposed generalized PtNSAF (GPtNSAF) is studied for the system identification problem via computer simulations. Specifically, we study the effects of using different numbers of subbands and various sparsity penalty terms for quasi-sparse, sparse, and dispersive systems. The results show that the benefit of increasing the number of subbands is larger than promoting sparsity of the estimated filter coefficients when the target system is quasi-sparse or dispersive. On the other hand, for sparse target systems, promoting sparsity becomes more important. More importantly, the two aspects provide complementary and additive benefits to the GPtNSAF for speeding up convergence. 

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2. On Mitigating Acoustic Feedback in Hearing Aids with Frequency Warping by All-Pass Networks

   https://doi.org/10.21437/INTERSPEECH.2019-3195  

   Lee, Ching-Hua; Chen, Kuan-Lin; Harris, Fred; Rao, Bhaskar D.; Garudadri, Harinath (September 2019, Proceedings Article published 15 Sep 2019 in Interspeech 2019)

   Acoustic feedback control continues to be a challenging prob- lem due to the emerging form factors in advanced hearing aids (HAs) and hearables. In this paper, we present a novel use of well-known all-pass filters in a network to perform frequency warping that we call “freping.” Freping helps in breaking the Nyquist stability criterion and improves adaptive feedback can- cellation (AFC). Based on informal subjective assessments, dis- tortions due to freping are fairly benign. While common ob- jective metrics like the perceptual evaluation of speech quality (PESQ) and the hearing-aid speech quality index (HASQI) may not adequately capture distortions due to freping and acoustic feedback artifacts from a perceptual perspective, they are still instructive in assessing the proposed method. We demonstrate quality improvements with freping for a basic AFC (PESQ: 2.56 to 3.52 and HASQI: 0.65 to 0.78) at a gain setting of 20; and an advanced AFC (PESQ: 2.75 to 3.17 and HASQI: 0.66 to 0.73) for a gain of 30. From our investigations, freping provides larger improvement for basic AFC, but still improves overall system performance for many AFC approaches. 

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3. Adaptive Subband Compression for Streaming of Continuous Point-on-Wave and PMU Data

   https://doi.org/10.1109/TPWRS.2021.3072882  

   Wang, Xinyi; Liu, Yilu; Tong, Lang (January 2021, IEEE Transactions on Power Systems)

   null (Ed.)

   A data compression system capable of providing real-time streaming of high-resolution continuous point-on-wave (CPOW) and phasor measurement unit (PMU) measurements is proposed. Referred to as adaptive subband compression (ASBC), the proposed technique partitions the signal space into subbands and adaptively compresses subband signals based on each subband's active bandwidth. The proposed technique conforms to existing industry phasor measurement standards, making it suitable for streaming high-resolution CPOW and PMU data either in continuous or burst on-demand/event-triggered modes. Experiments on synthetic and real data show that ASBC reduces the CPOW sampling rates by several orders of magnitude for real-time streaming while maintaining the precision required by industry standards. 

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4. Proportionate Adaptive Filters Based on Minimizing Diversity Measures for Promoting Sparsity

   https://doi.org/10.1109/IEEECONF44664.2019.9048716  

   Lee, Ching-Hua; Rao, Bhaskar D.; Garudadri, Harinath (November 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers)

   In this paper, a novel way of deriving proportionate adaptive filters is proposed based on diversity measure minimization using the iterative reweighting techniques well-known in the sparse signal recovery (SSR) area. The resulting least mean square (LMS)-type and normalized LMS (NLMS)-type sparse adaptive filtering algorithms can incorporate various diversity measures that have proved effective in SSR. Furthermore, by setting the regularization coefficient of the diversity measure term to zero in the resulting algorithms, Sparsity promoting LMS (SLMS) and Sparsity promoting NLMS (SNLMS) are introduced, which exploit but do not strictly enforce the sparsity of the system response if it already exists. Moreover, unlike most existing proportionate algorithms that design the step-size control factors based on heuristics, our SSR-based framework leads to designing the factors in a more systematic way. Simulation results are presented to demonstrate the convergence behavior of the derived algorithms for systems with different sparsity levels. 

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5. Negative Binomial Optimization for Low-Count Overdispersed Sparse Signal Reconstruction

   https://doi.org/10.23919/EUSIPCO58844.2023.10289862  

   Lu, Yu; Marcia, Roummel F. (September 2023, 2023 European Signal Processing Conference (EUSIPCO))

   Low-photon count imaging has been typically modeled by Poisson statistics. This discrete probability distribution model assumes that the mean and variance of a signal are equal. In the presence of greater variability in a dataset than what is expected, the negative binomial distribution is a suitable overdispersed alternative to the Poisson distribution. In this work, we present a framework for reconstructing sparse signals in these low-count overdispersed settings. Specifically, we describe a gradient-based sequential quadratic optimization approach that minimizes the negative log-likelihood corresponding to the negative binomial distribution coupled with a sparsity-promoting regularization term. Numerical experiments on 1D and 2D sparse/compressible signals are presented. 

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