Today, we are often surrounded by devices with one or more microphones, such as smartphones, laptops, and wireless microphones. If they are part of an acoustic sensor network, their distribution in the environment can be beneficially exploited for various speech processing tasks. However, applications like speaker localization, speaker tracking, and speech enhancement by beamforming avail themselves of the geometrical configuration of the sensors. Therefore, acoustic microphone geometry calibration has recently become a very active field of research. This article provides an application-oriented, comprehensive survey of existing methods for microphone position self-calibration, which will be categorized by the measurements they use and the scenarios they can calibrate. Selected methods will be evaluated comparatively with real-world recordings.

Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms

The rich literature on acoustic source localization mostly relies on the assumption of a constant value for the speed of sound. This hypothesis allows establishing simple relations between range differences and time differences and leads to effective estimation algorithms. However, it must be challenged for certain applications of wireless acoustic sensor networks in multizone buildings and outdoor environments. This article revisits the source localization problem for the more general case of an unknown value for the speed of sound. It reviews the physical foundations for the dependence of the speed of sound on the air temperature and presents the essential approaches to acoustic source localization. On this basis, several methods for source localization under uncertain or variable speed of sound conditions from the literature are discussed. Applications from different fields are shown. They comprise the localization of sources, sensors, and reflecting surfaces, time-difference-of-arrival disambiguation, and the direct determination of the speed of sound or the air temperature from acoustic measurements.

/pdf/acoustic-source-localization-under-variable-speed-of-sound-1rd10e0tgc.pdf

Acoustic Source Localization under Variable Speed of Sound Conditions

The propagating speed of waves depends on the physical properties of the transmitting material. Since these properties can vary along the propagation path, they cannot be determined from local measurements. However, mean values of the propagation speed can be obtained from time measurements, either between distributed sources and sensors (Time Of Arrival, TOA) if both are synchronized or otherwise from time differences between distributed sensors (Time Difference Of Arrivals, TDOA). This contribution investigates the required assumptions for speed estimation from time measurements and provides closed-form solutions for the synchronized and unsynchronized case. Furthermore the achievable accuracy is determined in terms of Cramer-Rao bounds. The analysis is carried out for the propagation of sound waves in air, where the propagation speed varies with the air temperature. Example results from loudspeaker-microphone recordings are provided. However the closed-form relations apply also to the propagation of other types of waves in linear regimes. This manuscript extends previous work by the authors by providing closed-form solutions and by a parallel treatment of the TOA and the TDOA measurements.

Closed-form estimation of the speed of propagating waves from time measurements

In this paper we propose a method for the estimation of the propagation speed of sound waves from Time Of Arrivals (TOAs) observed at a sensor array. In our previous work we presented a speed estimation method based on Time Differences of Arrival (TDOAs), here we show that the same approach may be applied successfully to the TOA-based problem. The exploitation of TOA measurements is driven by applications where synchronization between source and receiver is available. The proposed method provides an estimate of the average propagation speed of the wave along the source-receiver paths. Such a speed estimate is evaluated and compared with the TDOA-based estimate by means of Cramer-Rao Bound analysis and simulations.

Sound speed estimation from Time Of Arrivals: Derivation and comparison with TDOA-based estimation

The acoustic vector sensor (AVS) can measure the acoustic pressure field’s spatial gradient, so it has directionality. But its channels may have nonideal gain/phase responses, which will severely degrade its performance in finding source direction. To solve this problem, in this study, a self-calibration algorithm based on all-phase FFT spectrum analysis is proposed. This method is “self-calibrated” because prior knowledge of the training signal’s arrival angle is not required. By measuring signals from different directions, the initial phase can be achieved by taking the all-phase FFT transform to each channel. We use the amplitude of the main spectrum peak of every channel in different direction to formulate an equation; the amplitude gain estimates can be achieved by solving this equation. In order to get better estimation accuracy, bearing difference of different training signals should be larger than a threshold, which is related to SNR. Finally, the reference signal’s direction of arrival can be estimated. This method is easy to implement and has advantage in accuracy and antinoise. The efficacy of this proposed scheme is verified with simulation results.

/pdf/a-novel-self-calibration-method-for-acoustic-vector-sensor-3nuegkwce5.pdf

A Novel Self-Calibration Method for Acoustic Vector Sensor

In this paper we present a methodology for the self-calibration of microphone arrays using Times of Arrival of acoustic signals produced by acoustic sources. Geometric constraints are derived from the measurements of the Times of Arrival. Microphone locations are then found by combining the geometric constraints. When multiple arrays are present in the acoustic scene, in many applications the knowledge of their mutual positions is required. If a priori information about the geometry of the array is given, the same framework can be easily adapted to infer the mutual positions of the arrays. Some experimental results and simulations prove the accuracy of the current work.

Self-calibration of microphone arrays from measurement of Times of Arrival of acoustic signals

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Self-calibration of microphone arrays from measurement of Times of Arrival of acoustic signals