Speaker Counting Model based on Transfer Learning from SincNet Bottleneck Layer

People counting techniques have been widely researched recently and many different types of sensors can be used in this context. In this paper, we propose a system based on a deep-learning model able to identify the number of people in the crowded scenarios through the speech sound. In a nutshell the system relies on two components: counting concurrent speakers in overlapping talking sound directly and clustering single-speaker sound by speaker-identity over time. Compared to previously proposed speaker-counting systems models that only cluster single-speaker sound, this system is more accurate and less vulnerable to the overlapping sound in the crowded environment. In addition, counting speakers in overlapping sound also gives the minimal number of speakers so that it also improves the counting accuracy in a quiet environment. Our methodology is inspired by the newly proposed SincNet deep neural network framework which proves to be outstanding and highly efficient in sound processing with raw signals. By transferring the bottleneck layer of SincNet model as features fed to our speaker clustering model we reached a noticeably better performance than previous models who rely on the use MFCC and other engineered features.