ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2016 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics.
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- Read more about RSS-BASED SENSOR LOCALIZATION IN UNDERWATER ACOUSTIC SENSOR NETWORKS
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Since the global positioning system (GPS) is not applicable underwater, source localization using wireless sensor networks (WSNs) is gaining popularity in oceanographic applications. Unlike terrestrial WSNs (TWSNs) which use electromagnetic signaling, underwater WSNs (UWSNs) require underwater acoustic (UWA) signaling. Received signal strength (RSS)-based source localization is considered in this paper due to its practical simplicity and the constraint of low-cost sensor devices, but this area received little attention so far because of the complicated UWA transmission loss (TL) phenomena.
RSSmain.pdf
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- Read more about Depth-aware Saliency Detection Using Discriminative Saliency Fusion
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Additional depth information from RGBD images is one of characteristics different from conventional 2D images. Saliency detection aims to detect the attractive objects to human viewers in an image. Generally, saliency cues from different features are measured and fused into a single saliency using a linear or experiential fusion formula. we introduce a multi-stage depth-aware saliency model to fuse multiple saliency maps in a discriminative method.
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- Read more about Automatic Chord Estimation on SeventhsBass Chord Vocabulary Using Deep Neural Network
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ICASSP2016.pdf
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This is an overview poster on designing a privacy-preserving randomized requantization scheme which simultaneously guarantees local differential privacy and reduces bit-rate of input signals.
For more information, please check out the publication at IEEE Xplore:
S. Xiong, A.D. Sarwate, and N.B. Mandayam, “Randomized requantization with local differential privacy,” in Proceedings of the 41st International Conference on Acoustics, Speech and Signal Processing, March 2016.
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![](https://sigport.org/sites/default/files/styles/list/public/Media%20Root/Untitled%20drawing.jpg?itok=qVo1LHvS)
This is an overview poster on designing a privacy-preserving randomized requantization scheme which simultaneously guarantees local differential privacy and reduces bit-rate of input signals.
For more information, please check out the publication at IEEE Xplore:
S. Xiong, A.D. Sarwate, and N.B. Mandayam, “Randomized requantization with local differential privacy,” in Proceedings of the 41st International Conference on Acoustics, Speech and Signal Processing, March 2016.
ICASSP2016.pdf
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![](https://sigport.org/sites/default/files/styles/list/public/Media%20Root/Untitled%20drawing.jpg?itok=qVo1LHvS)
This is an overview poster on designing a privacy-preserving randomized requantization scheme which simultaneously guarantees local differential privacy and reduces bit-rate of input signals.
For more information, please check out the publication at IEEE Xplore:
S. Xiong, A.D. Sarwate, and N.B. Mandayam, “Randomized requantization with local differential privacy,” in Proceedings of the 41st International Conference on Acoustics, Speech and Signal Processing, March 2016.
ICASSP2016.pdf
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- Read more about Manga-Specific Features and Latent Style Model for Manga Style Analysis
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A latent style model describing manga styles based on the proposed manga-specific features is constructed to facilitate novel style-based applications. Two manga-specific features, i.e., screentone features showing texture and shade, and panel features showing panel arrangement, are firstly proposed to describe manga pages. Based on the latent Dirichlet allocation technique, we discover latent style elements embedded in manga documents, which are described by visual words derived from manga-specific features.
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- Read more about News Story Clustering with Fisher Embedding
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An automatic news story clustering system is presented to facilitate efficient news browsing and summarization. We describe news content by considering both what objects appear and how these objects move in news stories. With Fisher embedding, we respectively encode local features, semantics features, and dense trajectories as Fisher vectors, based on which similarity between news stories can be well evaluated and thus better clustering performance can be obtained.
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- Read more about A SPEAKER ADAPTATION TECHNIQUE FOR GAUSSIAN PROCESS REGRESSION BASED SPEECH SYNTHESIS USING FEATURE SPACE TRANSFORM
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