Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning

Autor: Maozhong Fu, Jesper Rindom Jensen, Yuhan Li, Mads Grasboll Christensen

Publikováno v: Fu, M, Jensen, J R, Li, Y & Christensen, M G 2022, Sparse Modeling of The Early Part of Noisy Room Impulse Responses with Sparse Bayesian Learning . in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022-Proceedings ., 9746069, IEEE, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, vol. 2022-May, pp. 586-590, 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Virtual, Online, Singapore, 23/05/2022 . https://doi.org/10.1109/ICASSP43922.2022.9746069

A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of a RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing p(0 < p ≤ 1)-norm-based met

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c752be07e8fe3bea4fd242931f7ceb52
https://vbn.aau.dk/da/publications/676941b6-bda2-43f7-abf2-05d2f5e0dde7

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation

Autor: Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafal Wisniewski, Mads Grasboll Christensen

Publikováno v: Li, Q, Gundersen, J S, Tjell, K, Wisniewski, R & Christensen, M G 2022, Privacy-preserving distributed expectation maximization for gaussian mixture model using subspace perturbation . in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022-Proceedings . IEEE Signal Processing Society, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, vol. 2022-May, pp. 4263-4267, 47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022, Virtual, Online, Singapore, 23/05/2022 . https://doi.org/10.1109/ICASSP43922.2022.9746144

Privacy has become a major concern in machine learning. In fact, the federated learning is motivated by the privacy concern as it does not allow to transmit the private data but only intermediate updates. However, federated learning does not always g

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c78d4c722a189f427da1cc594b51c7a5

Performance Comparison of AR Codebook Training for Speech Processing

Autor: Changchun Bao, Jesper Kjar Nielsen, Mads Grasboll Christensen, Zihao Cui

Publikováno v: Cui, Z, Bao, C, Nielsen, J K & Christensen, M G 2020, Performance Comparison of AR Codebook Training for Speech Processing . in 2020 15th IEEE International Conference on Signal Processing (ICSP) ., 9320929, IEEE Signal Processing Society, IEEE International Conference on Signal Processing (ICSP), pp. 131-135, 15th IEEE International Conference on Signal Processing, 06/12/2020 . https://doi.org/10.1109/ICSP48669.2020.9320929

In this paper, different ways of training codebook containing autoregressive (AR) parameter vectors are discussed. The fundamental goal of the discussion is to investigate if the classical approach for training AR-codebooks by clustering line spectra

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b6f5f63e91e7cb2a2792ed5001dbce1d
https://doi.org/10.1109/icsp48669.2020.9320929

Convex optimization-based Privacy-Preserving Distributed Least Squares via Subspace Perturbation

Autor: Richard Heusdens, Mads Grasboll Christensen, Qiongxiu Li

Publikováno v: 28th European Signal Processing Conference (EUSIPCO 2020)
Li, Q, Heusdens, R & Christensen, M G 2021, Convex optimization-based Privacy-Preserving Distributed Least Squares via Subspace Perturbation . in 28th European Signal Processing Conference (EUSIPCO) ., 9287473, IEEE, Proceedings of the European Signal Processing Conference, pp. 2110-2114, 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, 18/01/2021 . https://doi.org/10.23919/Eusipco47968.2020.9287473
Aalborg University
EUSIPCO

Over the past decades, privacy-preservation has received considerable attention, not only as a consequence of regulations such as the General Data Protection Regulation in the EU, but also from the fact that people are more concerned about data abuse

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b696f9fad14a073550a938404b3d11ab
http://resolver.tudelft.nl/uuid:42a63d75-b3c8-4c14-b9b0-e676586a86ae

Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms

Autor: Qiongxiu Li, Jaron Skovsted Gundersen, Mads Grasboll Christensen, Richard Heusdens

Publikováno v: Li, Q, Gundersen, J S, Heusdens, R & Christensen, M G 2021, ' Privacy-Preserving Distributed Processing: Metrics, Bounds and Algorithms ', I E E E Transactions on Information Forensics and Security, vol. 16, 9316966, pp. 2090-2103 . https://doi.org/10.1109/TIFS.2021.3050064
IEEE Transactions on Information Forensics and Security, 16

Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decentralized fashion without violating privacy. Many algorithms can be ado

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::309460a85dda8efb73b9bc3e7456e55c