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Speaker counting is the task of estimating the number of people that aresimultaneously speaking in an audio recording. For several audio processingtasks such as speaker diarization, separation, localization and tracking,knowing the number of speakers at each timestep is a prerequisite, or at leastit can be a strong advantage, in addition to enabling a low latency processing.In a previous work, we addressed the speaker counting problem with amultichannel convolutional recurrent neural network which produces anestimation at a short-term frame resolution. In this work, we show that, for agiven frame, there is an optimal position in the input sequence for bestprediction accuracy. We empirically demonstrate the link between that optimalposition, the length of the input sequence and the size of the convolutionalfilters.

Multichannel CRNN for Speaker Counting: an Analysis of Performance