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Neural network for multi-exponential sound energy decay analysis.

The Journal of the Acoustical Society of America
An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on s...

Acoustic Based Footstep Detection in Pervasive Healthcare.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Passive detection of footsteps in domestic settings can allow the development of assistive technologies that can monitor mobility patterns of older adults in their home environment. Acoustic footstep detection is a promising approach for non-intrusiv...

Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep...

Classification of fNIRS data with LDA and SVM: a proof-of-concept for application in infant studies.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This study presents the implementation of a within-subject classification method, based on the use of Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM), for the classification of hemodynamic responses. Using a synthetic dataset tha...

Underwater acoustic target recognition using attention-based deep neural network.

JASA express letters
Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but ...

Robust North Atlantic right whale detection using deep learning models for denoising.

The Journal of the Acoustical Society of America
This paper proposes a robust system for detecting North Atlantic right whales by using deep learning methods to denoise noisy recordings. Passive acoustic recordings of right whale vocalisations are subject to noise contamination from many sources, s...

On training targets for deep learning approaches to clean speech magnitude spectrum estimation.

The Journal of the Acoustical Society of America
Estimation of the clean speech short-time magnitude spectrum (MS) is key for speech enhancement and separation. Moreover, an automatic speech recognition (ASR) system that employs a front-end relies on clean speech MS estimation to remain robust. Tra...

Convolutional neural network for single-sensor acoustic localization of a transiting broadband source in very shallow water.

The Journal of the Acoustical Society of America
When a broadband source of radiated noise transits past a fixed hydrophone, a Lloyd's mirror constructive/destructive interference pattern can be observed in the output spectrogram. By taking the spectrum of a (log) spectrum, the power cepstrum detec...

Deep learning classification for improved bicoherence feature based on cyclic modulation and cross-correlation.

The Journal of the Acoustical Society of America
This paper aims to present an improved bicoherence spectrum (IBS) combined with cyclic modulation spectrum (CMS) and cross-correlation that is suitable for classification of hydrophone signals involving deep learning (DL). First, the proposed feature...

[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].

HNO
BACKGROUND: Acoustic snoring sound analysis is a noninvasive method for diagnosis of the mechanical mechanisms causing snoring that can be performed during natural sleep. The objective of this work is development and evaluation of classification sche...