Abstract
We investigate the self-localization problem of an ad-hoc network of randomly distributed and independent devices in an open-space environment with low reverberation but heavy noise (e.g. smartphones recording videos of an outdoor event). Assuming a sufficient number of sound sources, we estimate the distance between a pair of devices from the extreme (minimum and maximum) time difference of arrivals (TDOAs) from the sources to the pair of devices without knowing the time offset. The obtained inter-device distances are then exploited to derive the geometrical configuration of the network. In particular, we propose a robust audio fingerprinting algorithm for noisy recordings and perform landmark matching to construct a histogram of the TDOAs of multiple sources. The extreme TDOAs can be estimated from this histogram. By using audio fingerprinting features, the proposed algorithm works robustly in very noisy environments. Experiments with free-field simulation and open-space recordings prove the effectiveness of the proposed algorithm.
Highlights
T HE diffusion of smartphones has created new opportunities for applications when multiple devices are used to spontaneously capture audio and video of real-world scenes [1]
It has been shown that the distance between a pair of devices can be directly computed without knowing the time offsets between the two devices from the time difference of arrivals (TDOAs) of the sound sources located at end-fire positions
In the obtained TDOA histogram, the peaks of the end-fire sources become obscured by the peaks of the noise signals, making it difficult to detect the correct extreme TDOA values
Summary
T HE diffusion of smartphones has created new opportunities for applications when multiple devices are used to spontaneously capture audio and video of real-world scenes [1]. It has been shown that the distance between a pair of devices can be directly computed without knowing the time offsets between the two devices from the time difference of arrivals (TDOAs) of the sound sources located at end-fire positions. In this paper we focus on sound-based device localization in an outdoor environment where mobile devices such as smartphones capture events Three features characterize such an acoustic scenario: the reverberation is typically low, the recording is typically noisy, and there are multiple sound sources. Using the same inter-device distance estimation framework and the same assumption on a sufficient number of sound sources and positions as in [10], [11], we propose a novel audio-fingerprinting-based extreme (minimum and maximum) TDOA estimation algorithm. While landmark-based audio fingerprinting has been widely used in music information retrieval [12] due to its robustness to noise, to the best of our knowledge this is the first time that audio fingerprinting is employed for extreme TDOA estimation
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