  Speaker counting is the task of estimating the number of people that are
simultaneously speaking in an audio recording. For several audio processing
tasks such as speaker diarization, separation, localization and tracking,
knowing the number of speakers at each timestep is a prerequisite, or at least
it can be a strong advantage, in addition to enabling a low latency processing.
In a previous work, we addressed the speaker counting problem with a
multichannel convolutional recurrent neural network which produces an
estimation at a short-term frame resolution. In this work, we show that, for a
given frame, there is an optimal position in the input sequence for best
prediction accuracy. We empirically demonstrate the link between that optimal
position, the length of the input sequence and the size of the convolutional
filters.
