AIMC Topic: Sound Spectrography

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Cough Sound Based Deep Learning Models for Diagnosis of COVID-19 Using Statistical Features and Time-Frequency Spectrum.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
This paper presents a deep learning model that can classify COVID-19 patients through cough sounds. The cough sound data were selected from the Cambridge data set which is a crowedsourced data set collected from the Cambridge COVID-19 sounds applicat...
Humans Cough Sound Spectrography Deep Learning ROC Curve COVID-19 SARS-CoV-2
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Audio Cough Analysis by Parametric Modelling of Weighted Spectrograms to Interpret the Output of Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
This study explores the feasibility of employing eXplainable Artificial Intelligence (XAI) methodologies for the analysis of cough patterns in respiratory diseases. A cohort of 20 adult patients, all presenting persistent cough as a symptom of respir...
Pulmonary Disease, Chronic Obstructive Neural Networks, Computer Cough Female Male Signal Processing, Computer-Assisted Middle Aged Aged Humans Sound Spectrography Convolutional Neural Networks Adult
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Using deep learning to track time × frequency whistle contours of toothed whales without human-annotated training data.

The Journal of the Acoustical Society of America Jul 1, 2023
Many odontocetes produce whistles that feature characteristic contour shapes in spectrogram representations of their calls. Automatically extracting the time × frequency tracks of whistle contours has numerous subsequent applications, including speci...
Algorithms Animals Humans Vocalization, Animal Whales Deep Learning Sound Spectrography
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Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles.

The Journal of the Acoustical Society of America Dec 1, 2022
This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck...
Cetacea Software Animals Sound Spectrography Vocalization, Animal Deep Learning
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Data driven source localization using a library of nearby shipping sources of opportunity.

JASA express letters Dec 1, 2021
A library of broadband (100-1000 Hz) channel impulse responses (CIRs) estimated between a short bottom-mounted vertical line array (VLA) in the Santa Barbara channel and selected locations along the tracks of 27 isolated transiting ships, cumulated o...
Sound Spectrography Ships Acoustics Sound Motion
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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...
Oceans and Seas Deep Learning Signal-To-Noise Ratio Noise Fourier Analysis Signal Processing, Computer-Assisted Sound Spectrography
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Detection of early reflections from a binaural activity map using neural networks.

The Journal of the Acoustical Society of America Oct 1, 2019
Human listeners localize sounds to their sources despite competing directional cues from early room reflections. Binaural activity maps computed from a running signal can provide useful information about the presence of room reflections, but must be ...
Acoustic Stimulation Sound Localization Sound Sound Spectrography Humans Neural Networks, Computer Signal Processing, Computer-Assisted Acoustics
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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...
Sleep Apnea Syndromes Sound Spectrography Snoring Machine Learning Noise Humans
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Deep convolutional network for animal sound classification and source attribution using dual audio recordings.

The Journal of the Acoustical Society of America Feb 1, 2019
This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size ...
Callithrix Signal Processing, Computer-Assisted Sound Spectrography Vocalization, Animal Neural Networks, Computer Animals
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Crackle and Breathing Phase Detection in Lung Sounds with Deep Bidirectional Gated Recurrent Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
In this paper, we present a method for event detection in single-channel lung sound recordings. This includes the detection of crackles and breathing phase events (inspiration/expiration). Therefore, we propose an event detection approach with spectr...
Respiratory Sounds Humans Sound Spectrography Heart Sounds Lung Neural Networks, Computer Respiration Sound
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