Abstract
The generalized cross-correlation with phase transform (GCC-PHAT) algorithm has proved to be useful for blindly estimating the direction of arrival of compact sound sources from microphone array recordings. In applications with distributions of partial sources, such as the tires of vehicles in urban environments, the GCC-PHAT needs to be improved, otherwise the detected sound directions change values between directions of the main sources or correspond to an intermediate value between these directions. This paper presents an extension of the GCC-PHAT, based on post-processing of the output delay matrix and on image processing techniques, in order to separately identify directions of the sound produced by the front and rear tires of moving vehicles. The proposed approach can be extended to identify the tire noise directions produced by vehicles with multiple axles. The algorithm performance is analyzed using pass-by measurements of two-axle vehicles, acquired by a two-microphone array. The experiments were conducted with passenger vehicles of four distinct models, running at different speeds. The experimental results show that the proposed method is able to estimate the vehicle speed with an average error of 10.8 km/h and the vehicle wheelbase with 26 cm on average. A possible application is multiple source characterization for parametric vehicle sound synthesis in auralization.
Highlights
Noise maps are the main tool for assessing the sound distribution in urban environments
From the results presented in [19], which compared several direction of arrival (DoA) estimation algorithms for car pass-by recordings, it was concluded that the best time difference of arrival (TDoA) estimates were obtained with the generalized crosscorrelation with phase transform (GCC-PHAT) method [20, 21]
We propose the two-source DoA estimation system depicted in Figure 3, where image processing techniques followed by a curve fitting method are appended to the single-source GCC-PHAT algorithm
Summary
Noise maps are the main tool for assessing the sound distribution in urban environments. It does not portray the real auditory perception of a given urban area, because it is essentially a visual tool displaying long-term sound level averages. Car noise emissions contain contributions from various separate sources, whose spectral content are distinct and spatially distributed throughout the vehicle. Among these sources are the tires, the engine and the exhaust system, the first one being dominant for vehicle speeds above 30 km/h [10,11,12]. This work aims at developing a signal processing method in order to track and separate noise
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