Requests that were submitted before our April 21 deadline will be processed notifications of acceptance will be sent out once the technical program for the conference is finalized (late May or early June). We are no longer accepting submission of requests for the presentation of SPL manuscripts. The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. IEEE Communications Letters publishes high-quality short papers that are focused on theoretical and experimental advances in the general area of communications. Approved requests for presentation must have one author/presenter register for the conference according to the ICIP 2014 registration instructions. ![]() To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, which adopts a coarse. SPL papers presented at ICIP 2014 will be included in the ICIP 2014 proceedings distributed at the conference to attendees however, as the papers are already published in IEEE Signal Processing Letters, the SPL papers will not be included in IEEE Xplore for the conference. The performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. SPL papers published on or after April 1, 2013, and SPL manuscripts accepted on or before March 31, 2014, are eligible for this presentation opportunity. The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. This study has two primary contributions: first, we propose a deep CNN architecture for environmental sound. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a steganalytic discriminative subnetwork. IEEE/ACM Transactions on Audio Speech and Language Processing. (ICIP 2024) 2024 IEEE International Conference on Image Processing. However, the relative scarcity of labeled data has impeded the exploitation of this family of high-capacity models. (ICME 2024) 2024 IEEE International Conference on Multimedia and Expo. Authors of IEEE Signal Processing Letters (SPL) papers will be given the opportunity to present their work at ICIP 2014, subject to space availability and approval by the Technical Program Chairs of ICIP 2014. The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-temporal patterns makes them well suited to environmental sound classification.
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