  Recently, an end-to-end two-dimensional sound source localization algorithm
with ad-hoc microphone arrays formulates the sound source localization problem
as a classification problem. The algorithm divides the target indoor space into
a set of local areas, and predicts the local area where the speaker locates.
However, the local areas are encoded by one-hot code, which may lose the
connections between the local areas due to quantization errors. In this paper,
we propose a new soft label coding method, named label smoothing, for the
classification-based two-dimensional sound source location with ad-hoc
microphone arrays. The core idea is to take the geometric connection between
the classes into the label coding process.The first one is named static soft
label coding (SSLC), which modifies the one-hot codes into soft codes based on
the distances between the local areas. Because SSLC is handcrafted which may
not be optimal, the second one, named dynamic soft label coding (DSLC), further
rectifies SSLC, by learning the soft codes according to the statistics of the
predictions produced by the classification-based localization model in the
training stage. Experimental results show that the proposed methods can
effectively improve the localization accuracy.
