  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.
For that purpose, we address the speaker counting problem with a multichannel
convolutional recurrent neural network which produces an estimation at a
short-term frame resolution. We trained the network to predict up to 5
concurrent speakers in a multichannel mixture, with simulated data including
many different conditions in terms of source and microphone positions,
reverberation, and noise. The network can predict the number of speakers with
good accuracy at frame resolution.
