The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.

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This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain. The challenge comprises four tracks evaluating diarization performance under two input conditions (single channel vs. multi-channel) and two segmentation conditions (diarization from a reference speech segmentation vs. diarization from scratch). In order to prevent participants from overtuning to a particular combination of recording conditions and conversational domain, recordings are drawn from a variety of sources ranging from read audiobooks to meeting speech, to child language acquisition recordings, to dinner parties, to web video. We describe the task and metrics, challenge design, datasets, and baseline systems for speech enhancement, speech activity detection, and diarization. 1 See, for instance, the release of IBM's diarization API in 2017. The feature worked well for simple cases, but when run by users on real inputs, the performance was found to be lacking, especially for overlaps, back-channels, and short turns.

This paper explores how the in-and out-domain probabilistic linear discriminant analysis (PLDA) speaker verification behave when enrolment and verification lengths are reduced. Experiment studies have found that when full-length utterance is used for evaluation, in-domain PLDA approach shows more than 28% improvement in EER and DCF values over out-domain PLDA approach and when short utterances are used for evaluation, the performance gain of in-domain speaker verification reduces at an increasing rate. Novel modified inter dataset variability (IDV) compensation is used to compensate the mismatch between in-and out-domain data and IDV-compensated out-domain PLDA shows respectively 26% and 14% improvement over out-domain PLDA speaker verification when SWB and NIST data are respectively used for S normalization. When the evaluation utterance length is reduced, the performance gain by IDV also reduces as short utterance evaluation data i-vectors have more variations due to phonetic variations when compared to the dataset mismatch between in-and out-domain data.

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The problem of approximating the surface spanning a given set of 3D points as a polyhedron of triangular faces (“triangulation”) is a significant one, and has many applications in the fields of computer graphics and computer vision. In this paper, several solutions to this problem are reviewed. These solutions can be grouped into two classes, and particular emphasis is given to the class of surfaces spanned by parallel planar contours. For a contour pair P 0 ,P 1 ,...P m−1 and Q 0 ,Q 1 ,...Q n−1 , a graph theoretic approach can be used to arrive at a class of solutions, each requiring exactly m+n steps to triangulate the pair. Existing methods (both rigorous and heuristic) for extracting a particular solution from this group are reviewed, and a new heuristic based on inter-contour coherence is proposed. This heuristic is being used in the field of Ultrasonic Non-destructive Evaluation to produce images of flaws in pressure vessels, and its performance is shown to compare favorably with methods of greater computational complexity. It is believed that this heuristic can also be used with success in industrial vision systems where similar contours are obtained using a laser range finder.

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden.