# High-Resolution Speaker Counting In Reverberant Rooms Using CRNN With Ambisonics Features

arXiv: https://arxiv.org/abs/2003.07839 · HTML: https://mlsanity.com/paper/2003.07839/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [LSTM-CNN Network for Audio Signature Analysis in Noisy Environments](https://mlsanity.com/paper/2312.07059/) (2023)
- [Multi-microphone Automatic Speech Segmentation in Meetings Based on Circular Harmonics Features](https://mlsanity.com/paper/2306.04268/) (2023)

## Abstract (arXiv)

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.