61 citations · 128 across the 17 of their papers we have counts for
17 papers
Frequency bin-wise single channel speech presence probability estimation using multiple DNNs
Shuai Tao, Himavanth Reddy, Jesper Rindom Jensen +1
In this work, we propose a frequency bin-wise method to estimate the single-channel speech presence probability (SPP) with multiple deep neural networks (DNNs) in the short-time Fo…
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training
Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen +1
This paper focuses on leveraging deep representation learning (DRL) for speech enhancement (SE). In general, the performance of the deep neural network (DNN) is heavily dependent o…
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell +2
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 on…
A deep representation learning speech enhancement method using -VAE
Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen +1
In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of t…
A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder
Yang Xiang, Jesper Lisby Højvang, Morten Højfeldt Rasmussen +1
Recently, variational autoencoder (VAE), a deep representation learning (DRL) model, has been used to perform speech enhancement (SE). However, to the best of our knowledge, curren…
Communication efficient privacy-preserving distributed optimization using adaptive differential quantization
Qiongxiu Li, Richard Heusdens, Mads Græsbøll Christensen
Privacy issues and communication cost are both major concerns in distributed optimization. There is often a trade-off between them because the encryption methods required for priva…
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation
Alfredo Esquivel Jaramillo, Jesper Kjær Nielsen, Mads Græsbøll Christensen
In a hybrid speech model, both voiced and unvoiced components can coexist in a segment. Often, the voiced speech is regarded as the deterministic component, and the unvoiced speech…
Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms
Qiongxiu Li, Jaron Skovsted Gundersen, Richard Heusdens +1
Privacy-preserving distributed processing has recently attracted considerable attention. It aims to design solutions for conducting signal processing tasks over networks in a decen…
A Speech Enhancement Algorithm based on Non-negative Hidden Markov Model and Kullback-Leibler Divergence
Yang Xiang, Liming Shi, Jesper Lisby Højvang +2
In this paper, we propose a novel supervised single-channel speech enhancement method combing the the Kullback-Leibler divergence-based non-negative matrix factorization (NMF) and…
Space Alternating Variational Estimation Based Sparse Bayesian Learning for Complex-value Sparse Signal Recovery Using Adaptive Laplace Priors
Zonglong Bai, Liming Shi, Jinwei Sun +1
Due to its self-regularizing nature and its ability to quantify uncertainty, the Bayesian approach has achieved excellent recovery performance across a wide range of sparse signal…
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
Qiongxiu Li, Richard Heusdens, Mads Græsbøll Christensen
As the modern world becomes increasingly digitized and interconnected, distributed signal processing has proven to be effective in processing its large volume of data. However, a m…
Signal-Adaptive and Perceptually Optimized Sound Zones with Variable Span Trade-Off Filters
Taewoong Lee, Jesper Kjær Nielsen, Mads Græsbøll Christensen
Creating sound zones has been an active research field since the idea was first proposed. So far, most sound zone control methods rely on either an optimization of physical metrics…
Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson's Disease Detection
Amir Hossein Poorjam, Mathew Shaji Kavalekalam, Liming Shi +4
The performance of voice-based Parkinson's disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by deg…
Bayesian Pitch Tracking Based on the Harmonic Model
Liming Shi, Jesper Kjaer Nielsen, Jesper Rindom Jensen +2
Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accu…
Model-based Speech Enhancement for Intelligibility Improvement in Binaural Hearing Aids
Mathew Shaji Kavalekalam, Jesper K. Nielsen, Jesper B. Boldt +1
Speech intelligibility is often severely degraded among hearing impaired individuals in situations such as the cocktail party scenario. The performance of the current hearing aid t…
A Variational EM Method for Pole-Zero Modeling of Speech with Mixed Block Sparse and Gaussian Excitation
Liming Shi, Jesper Kjær Nielsen, Jesper Rindom Jensen +1
The modeling of speech can be used for speech synthesis and speech recognition. We present a speech analysis method based on pole-zero modeling of speech with mixed block sparse an…
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)
V. Abrol, O. Absil, P. -A. Absil +58
The third edition of the "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) took place in Aalborg, the 4th largest city in Denmark s…