# LSTM-CNN Network for Audio Signature Analysis in Noisy Environments

arXiv: https://arxiv.org/abs/2312.07059 · HTML: https://mlsanity.com/paper/2312.07059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [Frame-based overlapping speech detection using Convolutional Neural Networks](https://mlsanity.com/paper/2001.09937/) (2020)
- [TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection](https://mlsanity.com/paper/2101.06915/) (2021)
- [High-Resolution Speaker Counting In Reverberant Rooms Using CRNN With Ambisonics Features](https://mlsanity.com/paper/2003.07839/) (2020)
- [Real-time Speaker counting in a cocktail party scenario using Attention-guided Convolutional Neural Network](https://mlsanity.com/paper/2111.00316/) (2021)

## Built on by (most cited first, 1 of 1)

- [Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models](https://mlsanity.com/paper/2407.14984/) (2024)

## Abstract (arXiv)

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.