2,424 research outputs found
Two-Dimensional Beam Tracing from Visibility Diagrams for Real-Time Acoustic Rendering
No full text We present an extension of the fast beam-tracing method presented in the work of Antonacci et al. (2008) for the simulation of acoustic propagation in reverberant environments that accounts for diffraction and diffusion. More specifically, we show how visibility maps are suitable for modeling propagation phenomena more complex than specular reflections. We also show how the beam-tree lookup for path tracing can be entirely performed on visibility maps as well. We then contextualize such method to the two different cases of channel (point-to-point) rendering using a headset, and the rendering of a wave field based on arrays of speakers. Finally, we provide some experimental results and comparisons with real data to show the effectiveness and the accuracy of the approach in simulating the soundfield in an environment.</p
Estimation of Acoustic Reflection Coefficients Through Pseudospectrum Matching
No full textEstimating the geometric and reflective properties of the environment is important for a wide range of applications of space-time audio processing, from acoustic scene analysis to room equalization and spatial audio rendering. In this manuscript, we propose a methodology for frequency-subband in-situ estimation of the reflection coefficients of planar surfaces. This is a rather challenging task, as the reflection coefficients depend on the frequency and the angle of incidence and their estimate is highly sensitive to background noise and interfering sources. Our method is based on the assumption that we know the geometry of the reflectors; the position and the radiation pattern of the source; the position and the spatial response of the array. Applying beamforming algorithms on a single set of measured sensor data, we estimate the angular distribution of the acoustic energy (angular pseudospectrum) that impinges on a microphone array. We then apply a two-step iterative estimation technique based on an Expectation-Maximization (EM) algorithm. The first step estimates the scaling factors. The second one infers the reflection coefficients from the scaling factors. Under the assumption of additive white Gaussian noise, we finally determine the reflection coefficients with a Maximum Likelihood (ML) estimation method. The effectiveness and the accuracy of the proposed technique are assessed through experiments based on measured data
Synthetic Training Set Generation using Text-To-Audio Models for Environmental Sound Classification
No full textIn recent years, text-to-audio models have revolutionized the field of automatic audio generation. This paper investigates their application in generating synthetic datasets for training data-driven models. Specifically, this study analyzes the performance of two environmental sound classification systems trained with data generated from text-to-audio models. We considered three scenarios: a) augmenting the training dataset with data generated by text-to-audio models; b) using a mixed training dataset combining real and synthetic text-driven generated data; and c) using a training dataset composed entirely of synthetic audio. In all cases, the performance of the classification models was tested on real data. Results indicate that text-to-audio models are effective for dataset augmentation, with consistent performance when replacing a subset of the recorded dataset. However, the performance of the audio recognition models drops when relying entirely on generated audio
On the problem of the existence for connecting trajectories under the action of gravitational and electromagnetic fields
No full textHyperbolic boiler tube leak location based on quaternary acoustic array
No full textEarly detection and location of a boiler leak help reduce possible equipment damage and productivity loss. In the present study, a four-element acoustic array and a set of hyperbolic equations were used to locate a power plant boiler leak.
Maximum likelihood (ML) and phase transformation (PHAT) estimators were used to localize the leak source. Error rate and root mean square error (RMSE) evaluation revealed the superiority of ML over PHAT in the noisy and lowly reverberant boiler environment.
To avoid distant source assumption, a genetic algorithm (GA) modified by an adaptive Gaussian mutation operator was used to search for the global hyperbolic optimum by probability calculations. The GA slightly outperformed the quasi-Newton method and required more time to converge. However, selecting a starting point near the true position is not simple in practice, and iterative process conver- gence is not assured in the quasi-Newton method.
Time delay estimator errors greatly influence localization accuracy. The quaternary plane array localization error was within the permitted range of 0.01 ms, whereas that of the stereo array was 0.1 ms. Compared with the quaternary plane, the stereo array was more robust and accurate, but required more time to converge
Eigenfrequency optimisation of free violin plates
No full textWe discuss how the modal response of violin plates changes as their shape varies. Starting with an accurate 3D scan of the top plate of a historic violin, we develop a parametric model that controls a smooth shaping of the interior of the plate, while guaranteeing that the boundary is the same as the original violin. This allows us to generate a family of violin tops whose shape can be smoothly controlled through various parameters that are meaningful to a violin maker: from the thickness in different areas of the top to the location, angle, and dimensions of the bass bar. We show that the interplay between the different parameters affects the eigenmodes of the plate frequencies in a nonlinear fashion. We also show that, depending on the parameters, the ratio between the fifth and the second eigenfrequencies can be set to match that used by celebrated violin makers of the Cremonese school. As the parameterisation that we define can be readily understood by violin makers, we believe that our findings can have a relevant impact on the violin making community, as they show how to steer geometric modifications of the violin to balance the eigenfrequencies of the free plates
A Physics-Informed Neural Network Approach for Nearfield Acoustic Holography
No full textIn this manuscript, we describe a novel methodology for nearfield acoustic holography (NAH). The proposed technique is based on convolutional neural networks, with autoencoder architecture, to reconstruct the pressure and velocity fields on the surface of the vibrating structure using the sampled pressure soundfield on the holographic plane as input. The loss function used for training the network is based on a combination of two components. The first component is the error in the reconstructed velocity. The second component is the error between the sound pressure on the holographic plane and its estimate obtained from forward propagating the pressure and velocity fields on the structure through the Kirchhoff–Helmholtz integral; thus, bringing some knowledge about the physics of the process under study into the estimation algorithm. Due to the explicit presence of the Kirchhoff–Helmholtz integral in the loss function, we name the proposed technique the Kirchhoff–Helmholtz-based convolutional neural network, KHCNN. KHCNN has been tested on two large datasets of rectangular plates and violin shells. Results show that it attains very good accuracy, with a gain in the NMSE of the estimated velocity field that can top 10 dB, with respect to state-of-the-art techniques. The same trend is observed if the normalized cross correlation is used as a metric
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