The proliferation of smartphones nowadays has enabled many crowd assisted applications including audio-based sensing. In such applications, detected sound sources are meaningless without location information. However, it is challenging to localize sound sources accurately in a crowd using only microphones integrated in smartphones without existing infrastructures, such as dedicated microphone sensor systems. The main reason is that a smartphone is a nondeterministic platform that produces large and unpredictable variance in data measurements. Most existing localization methods are deterministic algorithms that are ill suited or cannot be applied to sound source localization using only smartphones. In this paper, we propose a distributed localization scheme using nondeterministic algorithms. We use the multiple possible outcomes of nondeterministic algorithms to weed out the effect of outliers in data measurements and improve the accuracy of sound localization. We then proposed to optimize the cost function using least absolute deviations rather than ordinary least squares to lessen the influence of the outliers. To evaluate our proposal, we conduct a testbed experiment with a set of 16 Android devices and 9 sound sources. The experiment results show that our nondeterministic localization algorithm achieves a root mean square error (RMSE) of 1.19 m, which is close to the Cramer-Rao bound (0.8 m). Meanwhile, the best RMSE of compared deterministic algorithms is 2.64 m.