1,327 research outputs found
Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise
Human-robot interaction relies on a noise-robust audio processing module
capable of estimating target speech from audio recordings impacted by
environmental noise, as well as self-induced noise, so-called ego-noise. While
external ambient noise sources vary from environment to environment, ego-noise
is mainly caused by the internal motors and joints of a robot. Ego-noise and
environmental noise reduction are often decoupled, i.e., ego-noise reduction is
performed without considering environmental noise. Recently, a variational
autoencoder (VAE)-based speech model has been combined with a fully adaptive
non-negative matrix factorization (NMF) noise model to recover clean speech
under different environmental noise disturbances. However, its enhancement
performance is limited in adverse acoustic scenarios involving, e.g. ego-noise.
In this paper, we propose a multichannel partially adaptive scheme to jointly
model ego-noise and environmental noise utilizing the VAE-NMF framework, where
we take advantage of spatially and spectrally structured characteristics of
ego-noise by pre-training the ego-noise model, while retaining the ability to
adapt to unknown environmental noise. Experimental results show that our
proposed approach outperforms the methods based on a completely fixed scheme
and a fully adaptive scheme when ego-noise and environmental noise are present
simultaneously.Comment: Accepted to the 2023 IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2023
Hyperbolic Wavelet-Fisz denoising for a model arising in Ultrasound Imaging
International audienceWe present an algorithm and its fully data-driven extension for noise reduction in ultrasound imaging. Our proposed method computes the hyperbolic wavelet transform of the image, before applying a multiscale variance stabilization technique, via a Fisz transformation. This adapts the wavelet coefficients statistics to the wavelet thresholding paradigm. The aim of the hyperbolic setting is to recover the image while respecting the anisotropic nature of structural details. The data-driven extension removes the need for any prior knowledge of the noise model parameters by estimating the noise variance using an isotonic Nadaraya-Watson estimator. Experiments on synthetic and real data, and comparisons with other noise reduction methods demonstrate the potential of our method at recovering ultrasound images while preserving tissue details. Finally, we emphasize the noise model we consider by applying our variance estimation procedure on real images
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