Search results for "Sound"
showing 10 items of 1845 documents
Role of expectancy in physiological responses to sound recognition of musical dissonance and timbral change
2021
Recently, it has been suggested that tonal violations produce greater skin conductance response (SCR) than timbral violations in music listening. However, it is unknown how people focus their attention during musical excerpts. The aim of this study is to replicate previous research considering two psychophysiological mechanisms: prediction error and brain stem reflex. Twenty-seven nonmusicians were instructed to listen six melodies and detect three altered conditions in one note: a dissonance (note out-of-key), a timbral change, and dissonance which changes in timbre and tone ( timdis). Amplitudes of SCR, heart rate (HR), and respiration rate (RSPR) were analyzed. In addition, the frequenc…
BLAST WAVES AT YASUR VOLCANO
2013
Infrasonic and seismic waveforms were collected during violent strombolian activity at Yasur Volcano (Vanuatu). Averaging similar to 3000 seismic events showed stable waveforms, evidencing a low-frequency (0.1-0.3Hz) signal preceding similar to 5-6s the explosion. Infrasonic waveforms were mostly asymmetric with a sharp compressive (5-106Pa) onset, followed by a small long-lasting rarefaction phase. Regardless of the pressure amplitude, the ratio between the positive and negative phases was constant. These waveform characteristics closely resembled blast waves. Infrared imagery showed an apparent cold spherical front similar to 20 m thick, which moved between 342 and 405m/s before the explo…
Statistical atlas based exudate segmentation
2013
Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent reference methods.
Inspection of additive-manufactured layered components
2015
Laser powder deposition (LPD) is a rapid additive manufacturing process to produce, layer upon layer, 3D geometries or to repair high-value components. Currently there is no nondestructive technique that can guarantee absence of flaws in LPD products during manufacturing. In this paper a laser ultrasonic technique for in-line inspection of LPD components is proposed. Reference samples were manufactured from Inconel and machined flaws were created to establish the sensitivity of the technique. Numerical models of laser-generated ultrasonic waves have been created to gain a deeper understanding of physics, to optimize the set-up and to verify the experimental measurements. Results obtained on…
A 2D-FEM Model of Nonlinear Ultrasound Propagation in Trans-cranial MRgFUS Technique
2022
Magnetic Resonance guided Focused Ultrasound (MRgFUS) is a non-invasive technique based on the thermal ablation of a target using high intensity focused ultrasound. MRgFUS treatment applied to brain is challenging due to the skull presence that attenuates ultrasound, leading to heating effects in bone region. In this study, we simulate trans-cranial nonlinear ultrasound propagation considering the detailed structure of bone tissue. We developed a 2D Finite Element (FE) model that mimics the propagation of focused ultrasound through skin, skull and brain tissue. The skull is represented as a three-layered system with two cortical tables packing a layer of trabecular bone. We assume that the …
Towards Responsible AI for Financial Transactions
2020
Author's accepted manuscript. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The application of AI in finance is increasingly dependent on the principles of responsible AI. These principles-explainability, fairness, privacy, accountability, transparency and soundness form the basis for trust in future AI systems. In this empirical study, we address the first p…
Learning With Context Feedback Loop for Robust Medical Image Segmentation
2021
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method for robust medical image segmentation by formulating the segmentation problem as a recurrent framework using two systems. The first one is a forward system of an encoder-decoder CNN that predicts the segmentation result from the input image. The predicted probabilistic output of the forward system …
Transfer Learning with Convolutional Networks for Atmospheric Parameter Retrieval
2018
The Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp satellite series provides important measurements for Numerical Weather Prediction (NWP). Retrieving accurate atmospheric parameters from the raw data provided by IASI is a large challenge, but necessary in order to use the data in NWP models. Statistical models performance is compromised because of the extremely high spectral dimensionality and the high number of variables to be predicted simultaneously across the atmospheric column. All this poses a challenge for selecting and studying optimal models and processing schemes. Earlier work has shown non-linear models such as kernel methods and neural networks perform w…
Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network
2020
In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use atten…
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic Environments
2020
The problem of training with a small set of positive samples is known as few-shot learning (FSL). It is widely known that traditional deep learning (DL) algorithms usually show very good performance when trained with large datasets. However, in many applications, it is not possible to obtain such a high number of samples. In the image domain, typical FSL applications include those related to face recognition. In the audio domain, music fraud or speaker recognition can be clearly benefited from FSL methods. This paper deals with the application of FSL to the detection of specific and intentional acoustic events given by different types of sound alarms, such as door bells or fire alarms, usin…