Abstract
We use audio fingerprinting to solve the synchronization problem between multiple recordings from an ad-hoc array consisting of randomly placed wireless microphones or handheld smartphones. Synchronization is crucial when employing conventional microphone array techniques such as beam-forming and source localization. We propose a fine audio landmark fingerprinting method that detects the time difference of arrivals (TDOAs) of multiple sources in the acoustic environment. By estimating the maximum and minimum TDOAs, the proposed method can accurately calculate the unknown time offset between a pair of microphone recordings. Experimental results demonstrate that the proposed method significantly improves the synchronization accuracy of conventional audio fingerprinting methods and achieves comparable performance to the generalized cross-correlation method.
Highlights
Ad-hoc acoustic sensor networks composed of randomly distributed wireless microphones or hand-held smartphones have been attracting increased interest due to their flexibility in sensor placement [1]
We show that by reducing the time-frequency analysis hop size, the classical landmark audio fingerprinting algorithm is able to detect the time difference of arrival (TDOA) of nearby sound sources that are captured by different microphones
Landmark-based fingerprinting has been refined to detect the TDOAs of the sources in a multi-source environment
Summary
Ad-hoc acoustic sensor networks composed of randomly distributed wireless microphones or hand-held smartphones have been attracting increased interest due to their flexibility in sensor placement [1]. The synchronization accuracy achieved by conventional audio fingerprinting methods is limited by the timefrequency analysis hop size, with typical values between a few and tens of milliseconds [10], which is enough for video applications but far below the sample-wise synchronization requirement in microphone array signal processing. We show that by reducing the time-frequency analysis hop size, the classical landmark audio fingerprinting algorithm is able to detect the time difference of arrival (TDOA) of nearby sound sources that are captured by different microphones. The TDOA information of multiple sources becomes ambiguous with such a hop size We further exploit this property to detect the maximum and minimum TDOAs around the microphone array, which can be used to further improve the synchronization accuracy. A sample-accuracy synchronization can be achieved with the proposed algorithm if a sufficient number of sound sources is located around the array
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