- Transducers
- Spatial and Multichannel Audio
- Source Separation and Signal Enhancement
- Room Acoustics and Acoustic System Modeling
- Network Audio
- Audio for Multimedia
- Audio Processing Systems
- Audio Coding
- Audio Analysis and Synthesis
- Active Noise Control
- Auditory Modeling and Hearing Aids
- Bioacoustics and Medical Acoustics
- Music Signal Processing
- Loudspeaker and Microphone Array Signal Processing
- Echo Cancellation
- Content-Based Audio Processing
- Read more about A Novel Thresholding Technique for the Denoising of Multicomponent Signals
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This paper addresses the issues of the denoising and retrieval of the components of multicomponent signals from their short-time Fourier transform (STFT). After having recalled the hard-thresholding technique, in the STFT context, we develop a new thresholding technique by exploiting some limitations of the former. Numerical experiments illustrating the benefits of the proposed method to retrieve the modes of noisy multicomponent signals conclude the paper.
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- Read more about Benchmarking Uncertainty Estimates with Deep Reinforcement Learning for Dialogue Policy Optimisation
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- Read more about SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS
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Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model.
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- Read more about Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation
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This paper presents a new adaptation of a Gaussian echo model (GEM) to estimate the distances to multiple targets using acoustic signals. The proposed algorithm utilizes m-sequences and opens the door for applying other modulations and signal designs for acoustic estimation in a similar way. The proposed algorithm estimates the system impulse response and uses the GEM to limit the effect of noise before applying deconvolution to estimate the time of arrival (TOA) to multiple targets with high accuracy.
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- Read more about NEURAL ADAPTIVE IMAGE DENOISER
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We propose a novel neural network-based adaptive image denoiser, dubbased as Neural AIDE. Unlike other neural network-based denoisers, which typically apply supervised training to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn context- based affine mappings that get applied to each noisy pixel. Our formulation enables using SURE (Stein’s Unbiased Risk Estimator)-like estimated losses of those mappings as empirical risks to minimize.
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- Read more about SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS
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Motivation:
Speech signal is an important information carrier in many social applications such as WeChat and GoogleTalk;
Modern digital technologies have put the security of speech at risk.
Solution: Watermarking is a promising solution to protect the speech signals by embedding digital data into them [1, 2].
Problem:
Many existing methods cannot satisfy the requirements of watermarking, e.g., inaudibility and robustness, simultaneously;
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- Read more about A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios
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BC_ICASSP.pdf
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- Read more about COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL
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Specific emitter identification (SEI) is gaining popularity since it can distinguish different individuals in same type of radar emitter under complex electromagnetic environment. However, classification of signals is still a challenging task when the feature has low physical representation. In this work, we propose a compressed sensing mask feature in ambiguity domain, which can significantly improve the recognition rate of civil flight radar emitters.
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