In the development of algorithms for sound source detection, identification and localization, having the possibility to generate datasets in a flexible and fast way is of utmost importance. However, most of the available acoustic simulators used for this purpose target indoor applications, and their usefulness is limited when it comes to outdoor environments such as that of a road, involving fast moving sources and long distances travelled by the sound waves. In this paper we present an acoustic propagation simulator specifically designed for road scenarios. In particular, the proposed Python software package enables to simulate the observed sound resulting from a source moving on an arbitrary trajectory relative to the observer, exploiting variable length delay lines to implement sound propagation and Doppler effect. An acoustic model of the road reflection and air absorption properties has been designed and implemented using digital FIR filters. The architecture of the proposed software is flexible and open to extensions, allowing the package to kick-start the implementation of further outdoor acoustic simulation scenarios.

Download

In designing a frequency tracker, the goal is to follow the continual time variation of the frequency from a particular sinusoidal component in a noisy signal with a high accuracy and a low sample delay. Although there exists a plethora of frequency trackers in the literature, in this paper, we focus on the particular class of frequency trackers that are built upon an adaptive notch filter (ANF), i.e. a constrained bi-quadratic infinite impulse response filter, where only a single parameter needs to be estimated. As opposed to using the conventional least-mean-square (LMS) algorithm, we present an alternative approach for the estimation of this parameter, which ultimately corresponds to the frequency to be tracked. Specifically, we reformulate the ANF in terms of a state-space model, where the state contains the unknown parameter and can be subsequently updated using a Kalman filter. We also demonstrate that such an approach is equivalent to doing a normalized LMS filter update, where the regularization parameter can be expressed as the ratio of the variance of the measurement noise to the variance of the prediction error. Through an evaluation with both simulated and realistic data, it is shown that in comparison to the LMS-updated frequency tracker, the proposed Kalmanupdated alternative, results in a more accurate performance, with a faster convergence rate, while maintaining a low computational complexity and the ability to be updated on a sample-by-sample basis.

Download