It is challenging to determine the directions of arrival of speech signals when there are fewer sensors than sources, particularly in noisy and reverberant environments. The coherence test by Mohan et al. exploits the time-frequency sparseness of non-stationary speech signals to select more relevant time-frequency bins to estimate directions of arrival. With no prior knowledge about the incoming sources, this work proposes a combination of noise-floor tracking, onset detection and a coherence test to robustly identify time-frequency bins where only one source is dominant. After that, the largest eigenvectors of covariance matrices corresponding to these bins are clustered and the directions of arrival of the sources are estimated based on the cluster centroids. Simulation and experimental results show that this method is able to localize 8 sources with small errors using only 3 omnidirectional microphones. The proposed method is robust to background noise and reverberation.