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There are multiple applications to automatically count people and specifytheir gender at work, exhibitions, malls, sales, and industrial usage. Althoughcurrent speech detection methods are supposed to operate well, in mostsituations, in addition to genders, the number of current speakers is unknownand the classification methods are not suitable due to many possible classes.In this study, we focus on a long-short-term memory convolutional neuralnetwork (LSTM-CNN) to extract time and / or frequency-dependent features of thesound data to estimate the number / gender of simultaneous active speakers ateach frame in noisy environments. Considering the maximum number of speakers as10, 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 ofconcept shows promising performance with training/validation MSE values ofabout 0.019/0.017 in detecting count and gender.

LSTM-CNN Network for Audio Signature Analysis in Noisy Environments