  The task of estimating the maximum number of concurrent speakers from single
channel mixtures is important for various audio-based applications, such as
blind source separation, speaker diarisation, audio surveillance or auditory
scene classification. Building upon powerful machine learning methodology, we
develop a Deep Neural Network (DNN) that estimates a speaker count. While DNNs
efficiently map input representations to output targets, it remains unclear how
to best handle the network output to infer integer source count estimates, as a
discrete count estimate can either be tackled as a regression or a
classification problem. In this paper, we investigate this important design
decision and also address complementary parameter choices such as the input
representation. We evaluate a state-of-the-art DNN audio model based on a
Bi-directional Long Short-Term Memory network architecture for speaker count
estimations. Through experimental evaluations aimed at identifying the best
overall strategy for the task and show results for five seconds speech segments
in mixtures of up to ten speakers.
