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1. Convolutional beamspace for linear arrays

   https://doi.org/DOI: 10.1109/TSP.2020.3021670  

   Chen, P.C ; Vaidyanathan, P.P ( September 2020 , IEEE transactions on signal processing)

   null (Ed.)

   Anew beamspace method for array processing, called convolutional beamspace (CBS), is proposed. It enjoys the advantages of classical beamspace such as lower computational complexity, increased parallelism of subband processing, and improved resolution threshold for DOA estimation. But unlike classical beamspace methods, it allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure and the shift-invariance are preserved under the CBS transformation. The method produces more accurate DOA estimates than classical beamspace, and for correlated sources, better estimates than element-space. The method also generalizes to sparse arrays by effective use of the difference coarray. For this, the autocorrelation evaluated on theULAportion of the coarray is filtered appropriately to produce the coarray CBS. It is also shown how CBS can be used in the context of sparse signal representation with dictionaries, where the dictionaries have columns that resemble steering vectors at a dense grid of frequencies. Again CBS processing with dictionaries offers better resolution, accuracy, and lower computational complexity. As only the filter responses at discrete frequencies on the dictionary grid are relevant, the problem of designing discrete-frequency FIR filters is also addressed. 

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2. Convolutional Beamspace and Sparse Signal Recovery for Linear Arrays

   https://doi.org/10.1109/IEEECONF51394.2020.9443522  

   Chen, Po-Chih ; Vaidyanathan, P. P. ( November 2020 , Proc. Asil. Conf. Sig., Sys., and Comp)

   null (Ed.)

   The convolutional beamspace (CBS) method for DOA estimation using dictionary-based sparse signal recovery is introduced. Beamspace methods enjoy lower computational complexity, increased parallelism of subband processing, and improved DOA resolution. But unlike classical beamspace methods, CBS allows root-MUSIC and ESPRIT to be performed directly for ULAs without additional preparation since the Vandermonde structure for ULAs are preserved in the CBS output. Due to the same reason, it is shown in this paper that sparse signal representation problems can also be directly formulated on the CBS output. Significant reduction in computational complexity and higher probability of resolution are obtained by using CBS. It is also shown how the regularization parameter involved in the method should be chosen 

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3. Sliding-Capon Based Convolutional Beamspace for Linear Arrays

   https://doi.org/10.1109/ICASSP39728.2021.9414022  

   Chen, Po-Chih ; Vaidyanathan, P P ( June 2021 , Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc)

   null (Ed.)

   A new method to design the filter for convolutional beamspace (CBS), called Capon-CBS, is proposed. The idea is to design the filter to be a sliding Capon beamformer. Such design takes input statistics into account, so it can do a better job of suppressing the sources that fall in the stopband. Capon-CBS can offer higher probability of resolution and smaller mean square error for DOA estimation, as demonstrated in the simulations. Moreover, like traditional CBS, Capon-CBS also has the advantage of low computational complexity 

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4. Estimating Acoustic Direction of Arrival Using a Single Structural Sensor on a Resonant Surface

   https://doi.org/10.1109/ICASSP49357.2023.10095986  

   DiPassio, Tre ; Heilemann, Michael C. ; Thompson, Benjamin ; Bocko, Mark F. ( June 2023 , ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   The direction of arrival (DOA) of an acoustic source is a signal characteristic used by smart audio devices to enable signal enhancement algorithms. Though DOA estimations are traditionally made using a multi-microphone array, we propose that the resonant modes of a surface excited by acoustic waves contain sufficient spatial information that DOA may be estimated using a singular structural vibration sensor. In this work, sensors are affixed to an acrylic panel and used to record acoustic noise signals at various angles of incidence. From these recordings, feature vectors containing the sums of the energies in the panel’s isolated modal regions are extracted and used to train deep neural networks to estimate DOA. Experimental results show that when all 13 of the acrylic panel’s isolated modal bands are utilized, the DOA of incident acoustic waves for a broadband noise signal may be estimated by a single structural sensor to within ±5° with a reliability of 98.4%. The size of the feature set may be reduced by eliminating the resonant modes that do not have strong spatial coupling to the incident acoustic wave. Reducing the feature set to the 7 modal bands that provide the most spatial information produces a reliability of 89.7% for DOA estimates within ±5° using a single sensor. 

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5. Astrid: accurate selectivity estimation for string predicates using deep learning

   https://doi.org/10.14778/3436905.3436907  

   Shetiya, Suraj ; Thirumuruganathan, Saravanan ; Koudas, Nick ; Das, Gautam ( December 2020 , Proceedings of the VLDB Endowment)

   null (Ed.)

   Accurate selectivity estimation for string predicates is a long-standing research challenge in databases. Supporting pattern matching on strings (such as prefix, substring, and suffix) makes this problem much more challenging, thereby necessitating a dedicated study. Traditional approaches often build pruned summary data structures such as tries followed by selectivity estimation using statistical correlations. However, this produces insufficiently accurate cardinality estimates resulting in the selection of sub-optimal plans by the query optimizer. Recently proposed deep learning based approaches leverage techniques from natural language processing such as embeddings to encode the strings and use it to train a model. While this is an improvement over traditional approaches, there is a large scope for improvement. We propose Astrid, a framework for string selectivity estimation that synthesizes ideas from traditional and deep learning based approaches. We make two complementary contributions. First, we propose an embedding algorithm that is query-type (prefix, substring, and suffix) and selectivity aware. Consider three strings 'ab', 'abc' and 'abd' whose prefix frequencies are 1000, 800 and 100 respectively. Our approach would ensure that the embedding for 'ab' is closer to 'abc' than 'abd'. Second, we describe how neural language models could be used for selectivity estimation. While they work well for prefix queries, their performance for substring queries is sub-optimal. We modify the objective function of the neural language model so that it could be used for estimating selectivities of pattern matching queries. We also propose a novel and efficient algorithm for optimizing the new objective function. We conduct extensive experiments over benchmark datasets and show that our proposed approaches achieve state-of-the-art results. 

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