TSSL: Trusted Sound Source Localization

Despite significant advancements in the accuracy of sound source localization, the confidence and robustness of the results still require improvement. Conducting uncertainty estimation can effectively alleviate this problem, since it provides a measure of confidence in the sound source localization results. In this work, we propose a trusted sound source localization neural network which can generate the accurate localization results and reliable uncertainty estimations. In this approach, the subjective theory was employed for associating the Dirichlet distribution with the predictions obtained from the neural network, treating them as subjective opinions. This allows us to model the overall uncertainty by parameterizing the class probabilities of sound source position using a Dirichlet distribution. Moreover, the reliable evidence supporting for the robust and reliable sound source localization results can also be gathered by the proposed method. In sum, this approach enhances the robustness of the neural network when facing with the out-of-distribution samples. To comprehensively evaluate the proposed method in the aspect of accuracy and robustness, extensive experiments were conducted on both simulated and real-world datasets. The proposed method outperforms other competing methods in the performance of robustness and reliability.