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ICASSP 2021 - IEEE International Conference on Acoustics, Speech and Signal Processing is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2021 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Unmanned aerial vehicles (UAV) often rely on GPS for navigation. GPS signals, however, are very low in power and easily jammed or otherwise disrupted. This paper presents a method for determining the navigation errors present at the beginning of a GPS-denied period utilizing data from a synthetic aperture radar (SAR) system. This is accomplished by comparing an online-generated SAR image with a reference image obtained a priori.

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Microphone array calibration is required to accurately capture the information in an audio source recording. Existing calibration methods require expensive hardware and setup procedures to compute filters for correcting microphone responses. Typically, such methods struggle to extend measurement accuracy to low frequencies. As a result, the error due to microphone gain mismatch propagates to all the modes in the spherical harmonic domain representation of a signal.

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11 Views

Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image recognition applications. Grad-CAM is a popular solution that provides such a visualization by combining the activation maps obtained from the model.However, the average gradient-based terms deployed in this method under-estimates the contribution of the representations discovered by the model to its predictions.

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3 Views

Explainable AI (XAI) is an active research area to interpret a neural network’s decision by ensuring transparency and trust in the task-specified learned models.Recently,perturbation-based model analysis has shown better interpretation, but back-propagation techniques are still prevailing because of their computational efficiency. In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks.

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Existing physical model-based imaging methods for ultrasound elasticity reconstruction utilize fixed variational regularizers that may not be appropriate for the application of interest or may not capture complex spatial prior information about the underlying tissues. On the other hand, end-to-end learning-based methods count solely on the training data, not taking advantage of the governing physical laws of the imaging system.

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7 Views

Hypernasality refers to the perception of abnormal nasal resonances in vowels and voiced consonants. Estimation of hypernasality severity from connected speech samples involves learning a mapping between the frame-level features and utterance-level clinical ratings of hypernasality. However, not all speech frames contribute equally to the perception of hypernasality.

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3 Views

Text-based speech editors expedite the process of editing speech recordings by permitting editing via intuitive cut, copy, and paste operations on a speech transcript. A major drawback of current systems, however, is that edited recordings often sound unnatural because of prosody mismatches around edited regions. In our work, we propose a new context-aware method for more natural sounding text-based editing of speech.

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5 Views

Safe screening rules are powerful tools to accelerate iterative solvers in sparse regression problems. They allow early identification of inactive coordinates (i.e., those not belonging to the support of the solution) which can thus be screened out in the course of iterations. In this paper, we extend the GAP Safe screening rule to the L1-regularized Kullback-Leibler divergence which does not fulfill the regularity assumptions made in previous works. The proposed approach is experimentally validated on synthetic and real count data sets.

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