LSTM-CNN Network for Audio Signature Analysis in Noisy Environments
This addresses the need for automated people counting and gender specification in public and industrial settings, but it is incremental as it combines existing LSTM and CNN methods.
The paper tackled the problem of estimating the number and gender of simultaneous speakers in noisy environments using an LSTM-CNN network, achieving training and validation MSE values of about 0.019 and 0.017.
There are multiple applications to automatically count people and specify their gender at work, exhibitions, malls, sales, and industrial usage. Although current speech detection methods are supposed to operate well, in most situations, in addition to genders, the number of current speakers is unknown and the classification methods are not suitable due to many possible classes. In this study, we focus on a long-short-term memory convolutional neural network (LSTM-CNN) to extract time and / or frequency-dependent features of the sound data to estimate the number / gender of simultaneous active speakers at each frame in noisy environments. Considering the maximum number of speakers as 10, we have utilized 19000 audio samples with diverse combinations of males, females, and background noise in public cities, industrial situations, malls, exhibitions, workplaces, and nature for learning purposes. This proof of concept shows promising performance with training/validation MSE values of about 0.019/0.017 in detecting count and gender.