Results 31 to 40 of about 431,572 (197)
Convolutional Neural Networks with Recurrent Neural Filters [PDF]
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 2018 Accepted by EMNLP 2018 as a short ...
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Gated Graph Convolutional Recurrent Neural Networks [PDF]
2019 27th European Signal Processing Conference (EUSIPCO), 2019 Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically tailored to deal with these problems.
Luana Ruiz +2 more
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Direction of Arrival Estimation for Multiple Sound Sources Using Convolutional Recurrent Neural Network [PDF]
European Signal Processing Conference, 2017 This paper proposes a deep neural network for estimating the directions of arrival (DOA) of multiple sound sources. The proposed stacked convolutional and recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS) along with the DOA ...
Sharath Adavanne +2 more
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Attention based Convolutional Recurrent Neural Network for Environmental Sound Classification [PDF]
Chinese Conference on Pattern Recognition and Computer Vision, 2019 Environmental sound classification (ESC) is a challenging problem due to the complexity of sounds. The ESC performance is heavily dependent on the effectiveness of representative features extracted from the environmental sounds.
Zhichao Zhang +4 more
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Deep Learning and Music Adversaries [PDF]
, 2015 OA Monitor ExerciseOA Monitor ExerciseAn {\em adversary} is essentially an algorithm intent on making a classification system perform in some particular way given an input, e.g., increase the probability of a false negative.
STURM, BLT +5 more
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, 2023 126132In this work, we propose an end-to-end trainable recurrent neural network for stereo image compression. The recurrent neural network allows variable compression rates without retraining the network due to the iterative nature of the recurrent units.
Suleman, Hamid +3 more
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Recurrent convolutional neural networks for poet identification
Digital Scholarship in the Humanities, 2020 Abstract Deep neural networks have been widely used in various language processing tasks. Recurrent neural networks (RNNs) and convolutional neural networks (CNN) are two common types of neural networks that have a successful history in capturing temporal and spatial features of texts.
Momtazi, Saeedeh, Salami, Dariush
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Fast Graph Convolutional Recurrent Neural Networks [PDF]
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 This paper proposes a Fast Graph Convolutional Neural Network (FGRNN) architecture to predict sequences with an underlying graph structure. The proposed architecture addresses the limitations of the standard recurrent neural network (RNN), namely, vanishing and exploding gradients, causing numerical instabilities during training.
Sai Kiran Kadambari +1 more
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Convolutional-Recurrent Neural Networks for Speech Enhancement [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 ICASSP ...
Han Zhao 0002 +3 more
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WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-End Speech Enhancement [PDF]
IEEE Signal Processing Letters, 2020 Due to the simple design pipeline, end-to-end (E2E) neural models for speech enhancement (SE) have attracted great interest. In order to improve the performance of the E2E model, the local and sequential properties of speech should be efficiently taken ...
Tsun-An Hsieh +3 more
semanticscholar +1 more source