Acoustic imaging in confined and noisy environments using double layer Time Reversal and Field Separation Methods

International audienceMany imaging methods cannot localize precisely unstationary sources in confined and noisy environments. In this paper, the use of a Time Reversal acoustic sink (TRS) method is proposed, in conjunction with a Field Separation Method (FSM). The proposed time reversal (TR) process is based on the measurement of the sound pressure field and its normal derivative on a double layer hemispherical antenna, which bounds the region of interest (ROI). These data are time-reversed and numerically back-propagated to a surface, 0.5 cm away from the source plane. As most imaging methods, the efficiency of this process relies on the use of the most suitable Green functions, which depend on the propagating environment. A way to improve the TR process is to transform numerically the confined space problem into a free field case, for which the Green functions are well-known. The proposed FSM consists in expanding the measured fields on the spherical harmonics functions, thus allowing to compute the outgoing waves. This process allows a precise localization and characterization of the source placed under the antenna, using free-field Green functions. Thanks to this method, the influence of reverberation and acoustic fields radiated by sources outside the ROI can be suppressed. The measurements presented in this paper are performed in an anechoic room, using two acoustic sources. The first one to image in the ROI emits a filtered pulse and the second one, placed outside the ROI, is driven by a Gaussian white noise. In order to assess the reconstruction quality of the proposed imaging process, a reference field is measured in an anechoic room on the back-propagation surface, corresponding to the pressure values when the source laying in the ROI is radiating alone. Comparisons with back-propagated pressures using TRS in conjunction with FSM show a good accuracy both in space and time domains

A linear phase IIR filterbank for the radial filters of ambisonic recordings

International audienceHigher order Ambisonics decomposition of natural sound fields is often performed using spherical, rigid microphone array measurements, mainly because of its simple implementation[1-2]. All the electronic equipment can be conveniently placed inside the spherical measurement array, without affecting the scattered acoustic field. However, restitution systems for HOA sound field synthesis generally exhibit a much larger radius than measurement arrays. The well-known “bass-boost” effect is directly linked to this size discrepancy: low frequencies have to be amplified, especially for higher order components of the Ambisonics decomposition. The dynamic range for filtering purposes is limited, mainly by the signal-to-noise ratio of the microphone array. In order to overcome this problem, we developed two microphone array prototypes using analogic MEMS microphones, which have become a viable solution in a small packaging and with a reasonable price thanks to the growing use of these sensors in domotics and in the mobile phone industry. MEMS microphones from the same production batch exhibit very similar characteristics and can be used for array signal processing without any level or phase calibration. The two proposed prototypes are made of group of 4 MEMS microphones for the same sensor position to improve the signal-to-noise ratio by 6 dB. The first prototype is a 5-th order Ambisonics system (50 sensors – 200 mems – lie on a Lebedev grid) and the second prototype is a Mixed Order Ambisonics (MOA) system (42 sensors – 168 mems – 3-th order in 3D and 11-th order in 2D, 24 sensors in the equator of the sphere) [3].Nevertheless, this approach does not dispense from the need to filter higher order coefficients. A simple high-pass filtering on each order component is not sufficient, since this would not only cause losses in terms of amplitude and power but also would affect the loudness of the restitution. A filter bank is therefore needed to cut-off noise amplification at low frequencies and apply appropriate gains for loudness equalization. Baumgartner and al [4] proposed a non-linear phase filter bank based on Linkwitz-Riley IIR filters. In order to avoid group delay distortions, Zotter proposed a linear phase filter bank based on FIR filters and the use of fast block convolution [5]. This solution is although not very flexible, since the FIR strongly depend on the radius of the measurement array and on the filter bank’s cut-off frequencies. Any change in the measurement system require a new computation of each FIR filters corresponding. In the present paper, a linear phase IIR filter bank is implemented. Thanks to the use of local overlap and add time reversal blocks [6], the filter bank exhibits a linear phase delay which only depends on the time reversal blocksize. The proposed implementation of the filter bank allows to change in real-time the frequency bands and loudness equalization (diffuse or free field equalization) using of Faust programming language [7].[1] S. Bertet, J. Daniel, E. Parizet, L. Gros, and O. Warusfel, “Investigation of the perceived spatial resolution of higher order ambisonics sound fields: a subjective evaluation involving virtual and real 3D microphones”, AES 30th International Conference, Saariselkä, Finland, 2007 March 15–17.[2] J. Meyer and G. Elko, “A highly scalable spherical microphone array based on an orthonormal decomposition of the soundfield,” in Acoustics, Speech, and Signal Processing, 2002. Proceedings.(ICASSP’02). IEEE International Conference on, vol. 2, Orlando, FL, USA, 2002.[3] S. Favrot, M. Marschall , J. Käsbach , J. Buchholz, T. Weller, “Mixed-order Ambisonics recording and playback for improving horizontal directionality”, presented at the AES 131st convention, New York, USA, 2011. [4] R. Baumgartner, H. Pomberger, and M. Frank, “Practical Implementation of Radial Filters for Ambisonic Recordings”, in Proc. first International Conference on Spatial Audio, Detmold, Germany, 2011.[5] F. Zotter, “A Linear-Phase Filter-Bank Approach to Process Rigid Spherical Microphone Array Recordings”, Proceedings of Papers – 5th International Conference on Electrical, Electronic and Computing Engineering, IcETRAN 2018, Palić, Serbia, June 11 – 14, 2018[6] S.R Powell and P.M Chau, “A technique for realizing linear phase IIR filter”, IEEE transactions and signal processing, vol 29(11), november 1991, 2425-2435.[7] For Faust programming language, see https://faust.grame.fr

Source localization in reverberant rooms using Deep Learning and microphone arrays

International audienceSound sources localization (SSL) is a subject of active research in the field of multi-channel signal processing since many years, and could benefit from the emergence of data-driven approaches. In the present paper, we present our recent developments on the use of a deep neural network, fed with raw multichannel audio in order to achieve sound source localization in reverberating and noisy environments. This paradigm allows to avoid the simplifying assumptions that most traditional localization methods incorporate using source models and propagating models. However, for an efficient training process, supervised machine learning algorithms rely on large-sized and precisely labelled datasets. There is therefore a critical need to generate a large number of audio data recorded by microphone arrays in various environments. When the dataset is built either with numerical simulations or with experimental 3D soundfield synthesis, the physical validity is also critical. We therefore present an efficient tensor GPU-based computation of synthetic room impulse responses using fractional delays for image source models, and analyze the localization performances of the proposed neural network fed with this dataset, which allows a significant improvement in terms of SSL accuracy over the traditional MUSIC and SRP-PHAT methods

TimeScaleNet : a Multiresolution Approach for Raw Audio Recognition using Learnable Biquadratic IIR Filters and Residual Networks of Depthwise-Separable One-Dimensional Atrous Convolutions

International audienceIn the present paper, we show the benefit of a multi-resolution approach that allows to encode the relevant information contained in unprocessed time domain acoustic signals. TimeScaleNet aims at learning an efficient representation of a sound, by learning time dependencies both at the sample level and at the frame level. The proposed approach allows to improve the interpretability of the learning scheme, by unifying advanced deep learning and signal processing techniques. In particular, TimeScaleNet's architecture introduces a new form of recurrent neural layer, which is directly inspired from digital IIR signal processing. This layer acts as a learnable passband biquadratic digital IIR filterbank. The learnable filterbank allows to build a time-frequency-like feature map that self-adapts to the specific recognition task and dataset, with a large receptive field and very few learnable parameters. The obtained frame-level feature map is then processed using a residual network of depthwise separable atrous convolutions. This second scale of analysis aims at efficiently encoding relationships between the time fluctuations at the frame timescale, in different learnt pooled frequency bands, in the range of [20 ms ; 200 ms]. TimeScaleNet is tested both using the Speech Commands Dataset and the ESC-10 Dataset. We report a very high mean accuracy of 94.87 ± 0.24% (macro averaged F1-score : 94.9 ± 0.24%) for speech recognition, and a rather moderate accuracy of 69.71 ± 1.91% (macro averaged F1-score : 70.14 ± 1.57%) for the environmental sound classification task

Source localization and identification with a compact array of digital mems microphones

International audienceA compact microphone array was developed for source localization and identification. This planar array consists of an arrangement of 32 digital MEMS microphones, concentrated in an aperture of fewer than 10 centimeters, and connected to a computer by Ethernet (AVB protocol). 3D direction of arrival (DOA) localization is performed using the pressure and the particle velocity estimated at the center of the array. The pressure is estimated by averaging the signals of multiple microphones. We compare high order pressure finite differences to the Phase and Amplitude Gradient Estimation (PAGE) method for particle velocity estimation. This paper also aims at presenting a method for UAV detection using the developed sensor and supervised binary classification

Timescalenet: a multiresolution approcha for raw audio recognition

International audienceIn recent years, the use of Deep Learning techniques in audio signal processing has led the scientific community to develop machine learning strategies that allow to build efficient representations from raw waveforms for machine hearing tasks. In the present paper, we show the benefit of a multi-resolution approach : TimeScaleNet aims at learning an efficient representation of a sound, by learning time dependencies both at the sample level and at the frame level. At the sample level, TimeScaleNet's architecture introduces a new form of recurrent neural layer that acts as a learnable passband biquadratic digital IIR filterbank and self-adapts to the specific recognition task and dataset, with a large receptive field and very few learnable parameters. The obtained frame-level feature map is then processed using a residual network of depthwise separable atrous convolutions. This second scale of analysis allows to encode the time fluctuations at the frame timescale, in different learnt pooled frequency bands. In the present paper, TimeScaleNet is tested using the Speech Commands Dataset. We report a very high mean accuracy of 94.87±0.24% (macro averaged F1-score : 94.9 ± 0.24%) for this particular task

Kahler-Ricci flow on blowups along submanifolds

In this short note, we study the behavior of Kaher-Ricci flow on Kahler manifolds which contract divisors to smooth submanifolds. We show that the Kahler potentials are Holder continuous and the flow converges sequentially in Gromov-Hausdorff topology to a compact metric space which is homeomorphic to the base manifold

Synthesis of a Mach cone using a speaker array

International audienceThe interest of the authors concerns sniper detection using time-reversal techniques on the Mach cone in a reverberant urban environment. In order to setup a safe and reproducible experimental framework at a reduced scale, it is possible to synthesize a N-wave with a conical geometry by means of loudspeakers disposed along a hypothetical ring axis. The supersonic nature of the simulated displacement leads to a set of constraints, both on spatial and temporal samplings, correlated to the structure of the medium and to the digital sampling of the N-shaped signal. Those constraints are theoretically studied to ensure reconstruction of the conical wavefront. A rst experiment has been realized, that allowed the synthesis of a Mach wave using 15 speakers spaced by 4.36 cm. Taking into account the directivity of each speaker and the diraction eects due to the line array, the symmetry of revolution of the cone is studied. Since the loudspeakers are in their linear regime, nonlinear behaviors of the wave are no longer present. However, inverse ltering methods are possible for improving the quality of the signal. We show that it is possible to visualize the spatio-temporal evolution of the pressure eld in planes containing the ring axis using a linear microphone array mounted on a translation robot. Comparisons between experiments and simulations show encouraging results for the following. PACS no. 43.28.We, 43.28.M

Techniques de Focalisation par Retournement Temporel dans le Domaine Audible

National audienceCetté etude présente les propriétés de focalisation acous-tique spatio-temporelles par retournement temporel (RT) dans le domaine audible en fonction de l'environnement dans lequel l'expérience est menée. La possibilité de réaliser une focalisation par RT grâcè a un miroir com-posé d'un transducteur sera démontrée dans un milieu réverbérant. Malgré leuréléganceleurélégance et leur adaptivité, ces méthodes de focalisation ne permettent pas de focaliser en super-résolution. Le puits acoustique, pour la première fois mis en place dans le domaine audible permet, lui, d'obtenir une résolution accrue par rapport à la focalisation par RT classique et de vaincre la limite physique de diffraction imposée par cettedernì ere. Nos résultats expérimentaux montrent la possibilité de créer une tache focale à λ/7