AIMC Topic: Noise

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

The Journal of the Acoustical Society of America Jun 1, 2021
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...
Deep Learning Noise Signal-To-Noise Ratio Whales Neural Networks, Computer Animals
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On training targets for deep learning approaches to clean speech magnitude spectrum estimation.

The Journal of the Acoustical Society of America May 1, 2021
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...
Speech Noise Signal-To-Noise Ratio Speech Intelligibility Deep Learning Speech Perception
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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 Dec 1, 2019
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...
Acoustics Predictive Value of Tests Sound Localization Noise Neural Networks, Computer Water
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Deep learning classification for improved bicoherence feature based on cyclic modulation and cross-correlation.

The Journal of the Acoustical Society of America Oct 1, 2019
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...
Signal-To-Noise Ratio Fourier Analysis Oceans and Seas Noise Signal Processing, Computer-Assisted Sound Spectrography Deep Learning
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[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].

HNO Sep 1, 2019
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...
Humans Machine Learning Noise Sleep Apnea Syndromes Sound Spectrography Snoring
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Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference.

Trends in hearing Jan 1, 2019
This study investigated a method to adjust hearing-aid gain by use of a machine-learning algorithm that estimates the optimal setting of gain parameters based on user preference indicated in an iterative paired-comparison procedure. Twenty hearing-im...
Sound Hearing Aids Hearing Loss Adult Noise Double-Blind Method Hearing Loss, Sensorineural Patient Preference Auditory Perception Female Young Adult Humans Machine Learning
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A deep learning based segregation algorithm to increase speech intelligibility for hearing-impaired listeners in reverberant-noisy conditions.

The Journal of the Acoustical Society of America Sep 1, 2018
Recently, deep learning based speech segregation has been shown to improve human speech intelligibility in noisy environments. However, one important factor not yet considered is room reverberation, which characterizes typical daily environments. The...
Hearing Aids Hearing Loss, Sensorineural Perceptual Masking Deep Learning Noise Speech Intelligibility Young Adult Male Middle Aged Female Aged Humans Algorithms
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Use of a Deep Recurrent Neural Network to Reduce Wind Noise: Effects on Judged Speech Intelligibility and Sound Quality.

Trends in hearing Jan 1, 2018
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The RNN was trained using...
Speech Acoustics Auditory Threshold Hearing Aids Hearing Loss Speech Perception Wind Acoustics Young Adult Male Random Allocation Humans Noise Speech Intelligibility Neural Networks, Computer Female
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Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

Ear and hearing Jan 1, 2018
OBJECTIVE: We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI...
Humans Adult Male Female Young Adult Noise Signal-To-Noise Ratio Middle Aged Deafness Cochlear Implantation Deep Learning Cochlear Implants Child Speech Perception
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Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.

The Journal of the Acoustical Society of America Mar 1, 2017
Machine-learning based approaches to speech enhancement have recently shown great promise for improving speech intelligibility for hearing-impaired listeners. Here, the performance of three machine-learning algorithms and one classical algorithm, Wie...
Recognition, Psychology Audiometry, Speech Perceptual Masking Hearing Loss Persons with Hearing Disabilities Middle Aged Speech Perception Female Noise Humans Aged Signal Processing, Computer-Assisted Acoustic Stimulation Speech Intelligibility Neural Networks, Computer Male Electric Stimulation Hearing Aids Machine Learning
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