  Steered Response Power (SRP) is a widely used method for the task of sound
source localization using microphone arrays, showing satisfactory localization
performance on many practical scenarios. However, its performance is diminished
under highly reverberant environments. Although Deep Neural Networks (DNNs)
have been previously proposed to overcome this limitation, most are trained for
a specific number of microphones with fixed spatial coordinates. This restricts
their practical application on scenarios frequently observed in wireless
acoustic sensor networks, where each application has an ad-hoc microphone
topology. We propose Neural-SRP, a DNN which combines the flexibility of SRP
with the performance gains of DNNs. We train our network using simulated data
and transfer learning, and evaluate our approach on recorded and simulated
data. Results verify that Neural-SRP's localization performance significantly
outperforms the baselines.
