CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

Schröter, Hendrik; Rosenkranz, Tobias; Escalante-B., Alberto-N.; Aubreville, Marc; Maier, Andreas

doi:10.1109/icassp40776.2020.9053563

“…We split our noise and speech corpus on original signal level in a train, validation (dev) and test set. We use the same splittings as [3,5]. All results are based on the test set unless otherwise stated.…”

Section: Methodsmentioning

confidence: 99%

“…NR is an important feature of modern hearing aids or hearing assistance devices. Recent contributions to deeplearning based monaural speech enhancement [1,2,3,4,5] result in a huge improvement over conventional noise suppression approaches [6,7]. This makes it desirable to incorporate these approaches into HAs.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Online Noise Reduction on Embedded Devices Using Hierarchical Recurrent Neural Networks

Schröter¹,

Rosenkranz²,

et al. 2020

Self Cite

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Deep-learning based noise reduction algorithms have proven their success especially for non-stationary noises, which makes it desirable to also use them for embedded devices like hearing aids (HAs). This, however, is currently not possible with stateof-the-art methods. They either require a lot of parameters and computational power and thus are only feasible using modern CPUs. Or they are not suitable for online processing, which requires constraints like low-latency by the filter bank and the algorithm itself.In this work, we propose a mask-based noise reduction approach. Using hierarchical recurrent neural networks, we are able to drastically reduce the number of neurons per layer while including temporal context via hierarchical connections. This allows us to optimize our model towards a minimum number of parameters and floating-point operations (FLOPs), while preserving noise reduction quality compared to previous work. Our smallest network contains only 5 k parameters, which makes this algorithm applicable on embedded devices. We evaluate our model on a mixture of EUROM and a real-world noise database and report objective metrics on unseen noise.

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“…They already became popular and commonly used in all domains of signal processing. Applications in speech processing range from trainable filter-banks [57] to the integration of entire algorithms such as complex linear coding [58]. There is even a software library for differentiable signal processing [59] available.…”

Section: Known Operator Learningmentioning

confidence: 99%

Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future

Maier

¹

,

Köstler

²

,

Heisig

³

et al. 2022

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In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.

show abstract

“…They already became popular and commonly used in all domains of signal processing. Applications in speech processing range from trainable filter-banks [55] to the integration of entire algorithms such as complex linear coding [56]. There is even a software library for differentiable signal processing [57] available.…”

Section: Prior Knowledge Asmentioning

confidence: 99%

Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future

Maier,

Köstler,

Heisig

et al. 2021

Preprint

0

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In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially all applications in medical imaging and medical image analysis. As we will point out by numerous examples, hybrid models are taking over in image reconstruction and analysis. Even domains such as physical simulation and scanner and acquisition design are being addressed using machine learning grey box modelling approaches. Towards the end of the article, we will investigate a few future directions and point out relevant areas in which hybrid modelling, meta learning, and other domains will likely be able to drive the state-of-the-art ahead.

show abstract

CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

Cited by 9 publications

References 20 publications

Lightweight Online Noise Reduction on Embedded Devices Using Hierarchical Recurrent Neural Networks

Lightweight Online Noise Reduction on Embedded Devices Using Hierarchical Recurrent Neural Networks

Known operator learning and hybrid machine learning in medical imaging—a review of the past, the present, and the future

Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future

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