Simulation of the acoustics of coupled rooms by numerical resolution of a diffusion equation

Over the last few years, some studies showed that the acoustic energy density in closed or semi-closed spaces may be the solution of a diffusion equation. This theory allows non-uniform repartition of energy, and is especially relevant in room acoustics for long rooms or complex spaces such as networks of rooms. In this work, the three-dimensional diffusion equation is solved directly by using a finite-element solver. This approach is used to simulate the acoustics of coupled rooms in terms of spatial variations of intensity levels and sound decay. The obtained results match satisfactorily with a model based on the classical statistical theory of room acoustics, but it allows to perform a finer spatial description of the acoustics of coupled rooms

Fast evaluation of the Rayleigh integral and applications to inverse acoustics

In this paper we present a fast evaluation of the Rayleigh integral, which leads to fast and robust solutions in inverse acoustics. The method commonly used to reconstruct acoustic sources on a plane in space is Planar Nearfield Acoustic Holography (PNAH). Some of the most important recent improvements in PNAH address the alleviation of spatial windowing effects that arise due to the application of a Fast Fourier Transform to a finite spatial measurement grid. Although these improvements have led to an increase in the accuracy of the method, errors such as leakage and edge degradation can not be removed completely. Such errors do not occur when numerical models such as the Boundary Element Method (BEM) are used.Moreover, the forward models involved converge to the exact solution as the number of elements tends to infinity. However, the time and computer memory needed to solve these problems up to an acceptable accuracy is large. We present a fast (O(n log n) per iteration) and memory efficient (O(n)) solution to the planar acoustic problem by exploiting the fact that the transfer matrix associated with a numerical implementation of the Rayleigh integral is Toeplitz. In this paper we will address both the fundamentals of the method and its application in inverse acoustics. Special attention will be paid to comparison between experimental results from PNAH, IBEM and the proposed method

Greedy Search for Descriptive Spatial Face Features

Facial expression recognition methods use a combination of geometric and appearance-based features. Spatial features are derived from displacements of facial landmarks, and carry geometric information. These features are either selected based on prior knowledge, or dimension-reduced from a large pool. In this study, we produce a large number of potential spatial features using two combinations of facial landmarks. Among these, we search for a descriptive subset of features using sequential forward selection. The chosen feature subset is used to classify facial expressions in the extended Cohn-Kanade dataset (CK+), and delivered 88.7% recognition accuracy without using any appearance-based features.Comment: International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network

This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of them separately in the initial stages. We show that instead of concatenating the features of each channel into a single feature vector the network learns sound events in multichannel audio better when they are presented as separate layers of a volume. Using the proposed spatial features over monaural features on the same network gives an absolute F-score improvement of 6.1% on the publicly available TUT-SED 2016 dataset and 2.7% on the TUT-SED 2009 dataset that is fifteen times larger.Comment: Accepted for IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017

Diffusion in a weakly random Hamiltonian flow

We consider the motion of a particle governed by a weakly random Hamiltonian flow. We identify temporal and spatial scales on which the particle trajectory converges to a spatial Brownian motion. The main technical issue in the proof is to obtain error estimates for the convergence of the solution of the stochastic acceleration problem to a momentum diffusion. We also apply our results to the system of random geometric acoustics equations and show that the energy density of the acoustic waves undergoes a spatial diffusion

A fisheries acoustic multi-frequency indicator to inform on large scale spatial patterns of aquatic pelagic ecosystems

Fisheries acoustic instruments provide information on four major groups in aquatic ecosystems: fish with and without swim bladder (tertiary and quaternary consumers), fluidlike zooplankton (secondary consumers) and small gas bearing organisms such as larval fish and phytoplankton (predominantly primary producers). We entertain that this information is useable to describe the spatial structure of organism groups in pelagic ecosystems. The proposal we make is based on a multi-frequency indicator that synthesises in a single metric the shape of the acoustic frequency response of different organism groups, i.e. the dependence of received acoustic backscattered energy on emitting echosounder frequency. We demonstrate the development and interpretation of the multi-frequency indicator using simulated data. We then calculate the indicator for acoustic water-column survey data from the Bay of Biscay and use it to create reference maps for the spatial structure of the four scattering groups as well as their small scale spatial variability. These maps provide baselines for monitoring future changes in the structure of the pelagic ecosystem

Acoustic modeling using the digital waveguide mesh

The digital waveguide mesh has been an active area of music acoustics research for over ten years. Although founded in 1-D digital waveguide modeling, the principles on which it is based are not new to researchers grounded in numerical simulation, FDTD methods, electromagnetic simulation, etc. This article has attempted to provide a considerable review of how the DWM has been applied to acoustic modeling and sound synthesis problems, including new 2-D object synthesis and an overview of recent research activities in articulatory vocal tract modeling, RIR synthesis, and reverberation simulation. The extensive, although not by any means exhaustive, list of references indicates that though the DWM may have parallels in other disciplines, it still offers something new in the field of acoustic simulation and sound synth