Reflection-Aware Sound Source Localization

We present a novel, reflection-aware method for 3D sound localization in indoor environments. Unlike prior approaches, which are mainly based on continuous sound signals from a stationary source, our formulation is designed to localize the position instantaneously from signals within a single frame. We consider direct sound and indirect sound signals that reach the microphones after reflecting off surfaces such as ceilings or walls. We then generate and trace direct and reflected acoustic paths using inverse acoustic ray tracing and utilize these paths with Monte Carlo localization to estimate a 3D sound source position. We have implemented our method on a robot with a cube-shaped microphone array and tested it against different settings with continuous and intermittent sound signals with a stationary or a mobile source. Across different settings, our approach can localize the sound with an average distance error of 0.8m tested in a room of 7m by 7m area with 3m height, including a mobile and non-line-of-sight sound source. We also reveal that the modeling of indirect rays increases the localization accuracy by 40% compared to only using direct acoustic rays.Comment: Submitted to ICRA 2018. The working video is available at (https://youtu.be/TkQ36lMEC-M

Semi-Supervised Sound Source Localization Based on Manifold Regularization

Conventional speaker localization algorithms, based merely on the received microphone signals, are often sensitive to adverse conditions, such as: high reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in meeting rooms or cars, it can be assumed that the source position is confined to a predefined area, and the acoustic parameters of the environment are approximately fixed. Such scenarios give rise to the assumption that the acoustic samples from the region of interest have a distinct geometrical structure. In this paper, we show that the high dimensional acoustic samples indeed lie on a low dimensional manifold and can be embedded into a low dimensional space. Motivated by this result, we propose a semi-supervised source localization algorithm which recovers the inverse mapping between the acoustic samples and their corresponding locations. The idea is to use an optimization framework based on manifold regularization, that involves smoothness constraints of possible solutions with respect to the manifold. The proposed algorithm, termed Manifold Regularization for Localization (MRL), is implemented in an adaptive manner. The initialization is conducted with only few labelled samples attached with their respective source locations, and then the system is gradually adapted as new unlabelled samples (with unknown source locations) are received. Experimental results show superior localization performance when compared with a recently presented algorithm based on a manifold learning approach and with the generalized cross-correlation (GCC) algorithm as a baseline

Binaural Cues for Distance and Direction of Nearby Sound Sources

To a first-order approximation, binaural localization cues are ambiguous: a number of source locations give rise to nearly the same interaural differences. For sources more than a meter from the listener, binaural localization cues are approximately equal for any source on a cone centered on the interaural axis (i.e., the well-known "cones of confusion"). The current paper analyzes simple geometric approximations of a listener's head to gain insight into localization performance for sources near the listener. In particular, if the head is treated as a rigid, perfect sphere, interaural intensity differences (IIDs) can be broken down into two main components. One component is constant along the cone of confusion (and thus co varies with the interaural time difference, or ITD). The other component is roughly constant for a sphere centered on the interaural axis and depends only on the relative pathlengths from the source to the two ears. This second factor is only large enough to be perceptible when sources are within one or two meters of the listener. These results are not dramatically different if one assumes that the ears are separated by 160 degrees along the surface of the sphere (rather than diametrically opposite one another). Thus, for sources within a meter of the listener, binaural information should allow listeners to locate sources within a volume around a circle centered on the interaural axis, on a "doughnut of confusion." The volume of the doughnut of confusion increases dramatically with angle between source and the interaural axis, degenerating to the entire median plane in the limit.Air Force Office of Scientific Research (F49620-98-1-0108

A robust sequential hypothesis testing method for brake squeal localisation

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement. (C) 2019 Author(s)

Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on \textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table

Low-frequency sound source localization as a function of closed acoustic spaces

Further development of an emerging generalized theory of low-frequency sound localization in closed listening spaces is presented that aims to resolve the ambiguities inherent in previous research. The approach takes a robust set of equations based on source/listener location, reverberation time and room dimensions and tests them against a set of evaluation procedures to explore image location against theoretical expectations. Phantom imaging is germane to the methodology and its match within the theoretical framework is investigated. Binaural recordings are used to inspect a range of closed environments for localization clues each with a range of source-listener placements. A complementary series of small-scale listening tests are included for perceptual validation