AIMC Topic: Acoustics

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Insights into Materials, Physics, and Applications in Flexible and Wearable Acoustic Sensing Technology.

Advanced materials (Deerfield Beach, Fla.)
Sound plays a crucial role in the perception of the world. It allows to communicate, learn, and detect potential dangers, diagnose diseases, and much more. However, traditional acoustic sensors are limited in their form factors, being rigid and cumbe...

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring.

Physics in medicine and biology
Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, RA imaging signal often suffers from poo...

Clustering Methods for Vibro-Acoustic Sensing Features as a Potential Approach to Tissue Characterisation in Robot-Assisted Interventions.

Sensors (Basel, Switzerland)
This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signal...

Antropo: An open-source platform to increase the anthropomorphism of the Franka Emika collaborative robot arm.

PloS one
Robot-to-human communication is important for mutual understanding during human-robot collaboration. Most of the current collaborative robots (cobots) are designed with low levels of anthropomorphism. Therefore, the ability of cobots to express human...

Classification of cancer cells at the sub-cellular level by phonon microscopy using deep learning.

Scientific reports
There is a consensus about the strong correlation between the elasticity of cells and tissue and their normal, dysplastic, and cancerous states. However, developments in cell mechanics have not seen significant progress in clinical applications. In t...

Comparison of the prediction accuracy of machine learning algorithms in crosslinguistic vowel classification.

Scientific reports
Machine learning algorithms can be used for the prediction of nonnative sound classification based on crosslinguistic acoustic similarity. To date, very few linguistic studies have compared the classification accuracy of different algorithms. This st...

NEAL: an open-source tool for audio annotation.

PeerJ
Passive acoustic monitoring is used widely in ecology, biodiversity, and conservation studies. Data sets collected via acoustic monitoring are often extremely large and built to be processed automatically using artificial intelligence and machine lea...

DISCO: A deep learning ensemble for uncertainty-aware segmentation of acoustic signals.

PloS one
Recordings of animal sounds enable a wide range of observational inquiries into animal communication, behavior, and diversity. Automated labeling of sound events in such recordings can improve both throughput and reproducibility of analysis. Here, we...

Beyond Correlations: Deep Learning for Seismic Interferometry.

IEEE transactions on neural networks and learning systems
Passive seismic interferometry is a vastly generalized blind deconvolution question, where different paths through the Earth correspond to different channels called Green's functions; the sources are completely incoherent and not shared by the channe...

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

IEEE transactions on neural networks and learning systems
Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterizati...