Room geometry inference algorithms rely on the localization of acoustic reflectors to identify boundary surfaces of an enclosure. Rooms with highly absorptive walls or walls at large distances from the measurement setup pose challenges for such algorithms. As it is not always possible to localize all walls, we present a data-driven method to jointly detect and localize acoustic reflectors that correspond to nearby and/or reflective walls. A multi-branch convolutional recurrent neural network is employed for this purpose. The network's input consists of a time-domain acoustic beamforming map, obtained via Radon transform from multi-channel room impulse responses. A modified loss function is proposed that forces the network to pay more attention to walls that can be estimated with a small error. Simulation results show that the proposed method can detect nearby and/or reflective walls and improve the localization performance for the detected walls.
Aalborg University（奥尔堡大学）；Universite de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）
In this paper, we propose a method to estimate the proximity of an acoustic reflector, e.g., a wall, using ego-noise, i.e., the noise produced by the moving parts of a listening robot. This is achieved by estimating the times of arrival of acoustic echoes reflected from the surface. Simulated experiments show that the proposed nonintrusive approach is capable of accurately estimating the distance of a reflector up to 1 meter and outperforms a previously proposed intrusive approach under loud ego-noise conditions. The proposed method is helped by a probabilistic echo detector that estimates whe…
Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usu…
University of Science and Technology of China（中国科学技术大学）；iFlytek（科大讯飞）；Georgia Institute of Technology（佐治亚理工学院）
In this paper, we propose a novel four-stage data augmentation approach to ResNet-Conformer based acoustic modeling for sound event localization and detection (SELD). First, we explore two spatial augmentation techniques, namely audio channel swapping (ACS) and multi-channel simulation (MCS), to deal with data sparsity in SELD. ACS and MDS focus on augmenting the limited training data with expanding direction of arrival (DOA) representations such that the acoustic models trained with the augmented data are robust to localization variations of acoustic sources. Next, time-domain mixing (TDM) an…
Xiamen University（厦门大学）；University of Engineering and Technology (UET) Lahore（拉合尔工程技术大学）
Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source location. However, the model-based beamforming methods fail to achieve the high-resolution of conventional beamforming maps. Deep neural networks are also appropriate to locate the sound source, but in general, these methods with complex network structures are hard to be recognized by hardware. In this paper, a novel neural network, termed the Acoustic-Net, is proposed to locate and quantify the sound source simply u…
Passive acoustic non-line-of-sight localization without a relay surface
Department of Applied Physics, Hebrew University of Jerusalem（应用物理系，耶路撒冷希伯来大学）；Alexander Grass Center for Bioengineering, Hebrew University of Jerusalem（亚历山大·格拉斯生物工程中心，耶路撒冷希伯来大学）
The LOCATA Challenge: Acoustic Source Localization and Tracking
University of Southampton（南安普顿大学）；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Imperial College London（帝国理工学院）
On Adversarial Attacks In Acoustic Drone Localization
Department of Computer Science（计算机科学系）；Technion – Israel Institute of Technology（技术ion-以色列理工学院）；School of Electrical and Computer Engineering（电气与计算机工程学院）；Ben-Gurion University of the Negev（贝内尔谢瓦大学）；Institute of Science and Technology Austria（奥地利科学与技术研究院）
SAID: Semantic Acoustic Imaging Detector for Sound Event Localization and Detection
Nanjing University（南京大学）；The Chinese University of Hong Kong（香港中文大学）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；Shun Hing Institute of Advanced Engineering（信兴高等工程研究所）
A Head Related Transfer Function (HRTF) characterizes how a human ear receives sounds from a point in space, and depends on the shapes of one's head, pinna, and torso. Accurate estimations of HRTFs for human subjects are crucial in enabling binaural acoustic applications such as sound localization and 3D sound spatialization. Unfortunately, conventional approaches for HRTF estimation rely on specialized devices or lengthy measurement processes. This work proposes a novel lightweight method for HRTF individualization that can be implemented using commercial-off-the-shelf components and performe…
RV College of Engineering（RV工程学院）；Wipro Limited（威普罗有限公司）
This paper elucidates a model for acoustic single and multi-tone classification in resource constrained edge devices. The proposed model is of State-of-the-art Fast Accurate Stable Tiny Gated Recurrent Neural Network. This model has resulted in improved performance metrics and lower size compared to previous hypothesized methods by using lesser parameters with higher efficiency and employment of a noise reduction algorithm. The model is implemented as an acoustic AI module, focused for the application of sound identification, localization, and deployment on AI systems like that of an autonomou…
Sound Source Localization for Spatial Mapping of Surgical Actions in Dynamic Scenes
ROCS（罗克辛研究所）；Balgrist University Hospital（巴尔格里斯大学医院）；University of Zurich（苏黎世大学）；Dept. of Orthopedics（骨科部门）；Computer Vision and Geometry Group（计算机视觉与几何组）；ETH Zurich（苏黎世联邦理工学院）
Machine learning in acoustics: theory and applications
Scripps Institution of Oceanography, University of California San Diego（加州大学圣地亚哥分校斯克里普斯海洋研究所）；Massachusetts Institute of Technology（麻省理工学院）；San Diego State University（圣地亚哥州立大学）；Bar-Ilan University（巴伊兰大学）；University of California San Diego（加州大学圣地亚哥分校）
