AIMC Topic: Sound Spectrography

Clear Filters Showing 51 to 60 of 74 articles

The development of deep convolutional generative adversarial network to synthesize odontocetes' clicks.

The Journal of the Acoustical Society of America
Odontocetes are capable of dynamically changing their echolocation clicks to efficiently detect targets, and learning their clicking strategy can facilitate the design of man-made detecting signals. In this study, we developed deep convolutional gene...

Automated detection of Bornean white-bearded gibbon (Hylobates albibarbis) vocalizations using an open-source framework for deep learning.

The Journal of the Acoustical Society of America
Passive acoustic monitoring is a promising tool for monitoring at-risk populations of vocal species, yet, extracting relevant information from large acoustic datasets can be time-consuming, creating a bottleneck at the point of analysis. To address t...

Cough-DL: A Deep Learning Model for Ear-Worn Cough Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cough serves as a crucial bio-marker for evaluation and monitoring of pulmonary conditions. With growing interest towards automatic cough detection systems, it's important to acknowledge the existing hurdles on the way for a robust cough counter. The...

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
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...

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
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...

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
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...

Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles.

The Journal of the Acoustical Society of America
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...

Data driven source localization using a library of nearby shipping sources of opportunity.

JASA express letters
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...

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...

Detection of early reflections from a binaural activity map using neural networks.

The Journal of the Acoustical Society of America
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 ...