Smart City Gnosys
Smart city article details
| Title | An Ensemble Of Convolutional Neural Networks For Sound Event Detection |
|---|---|
| ID_Doc | 8076 |
| Authors | Mukhamadiyev A.; Khujayarov I.; Nabieva D.; Cho J. |
| Year | 2025 |
| Published | Mathematics, 13, 9 |
| DOI | http://dx.doi.org/10.3390/math13091502 |
| Abstract | Sound event detection tasks are rapidly advancing in the field of pattern recognition, and deep learning methods are particularly well suited for such tasks. One of the important directions in this field is to detect the sounds of emotional events around residential buildings in smart cities and quickly assess the situation for security purposes. This research presents a comprehensive study of an ensemble convolutional recurrent neural network (CRNN) model designed for sound event detection (SED) in residential and public safety contexts. The work focuses on extracting meaningful features from audio signals using image-based representation, such as Discrete Cosine Transform (DCT) spectrograms, Cocheagrams, and Mel spectrograms, to enhance robustness against noise and improve feature extraction. In collaboration with police officers, a two-hour dataset consisting of 112 clips related to four classes of emotional sounds, such as harassment, quarrels, screams, and breaking sounds, was prepared. In addition to the crowdsourced dataset, publicly available datasets were used to broaden the study’s applicability. Our dataset contains 5055 audio files of different lengths totaling 14.14 h and strongly labeled data. The dataset consists of 13 separate sound categories. The proposed CRNN model integrates spatial and temporal feature extraction by processing these spectrograms through convolution and bi-directional gated recurrent unit (GRU) layers. An ensemble approach combines predictions from three models, achieving F1 scores of 71.5% for segment-based metrics and 46% for event-based metrics. The results demonstrate the model’s effectiveness in detecting sound events under noisy conditions, even with a small, unbalanced dataset. This research highlights the potential of the model for real-time audio surveillance systems using mini-computers, offering cost-effective and accurate solutions for maintaining public order. © 2025 by the authors. |
| Author Keywords | audio signal; convolution neural network (CNN); data augmentation; DCT; deep learning; ensemble of classifiers; Mel; pattern recognition; smart city; sound event detection |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
17908  | 0.912 | Ciaburro G. | Deep Learning Methods For Audio Events Detection | Studies in Big Data, 82 (2021) |
| 60187 | 0.896 | Lakshmi R.; Chaitra N.C.; Thejaswini R.; Swapna H.; Parameshachari B.D.; Kumar S.D.S.; Puttegowda K. | Urban Sound Classification With Convolutional Neural Network | 2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024 (2024) |
| 19236 | 0.892 | Hattaraki S.M.; Kambalimath S.G.; Savukar B.P.; Bagali S.; Dixit U.D.; Jadhav A.S. | Detection And Classification Of Various Listening Environments For Hearing-Impaired Individuals Using Crnn | 2024 International Conference on Innovation and Novelty in Engineering and Technology, INNOVA 2024 - Proceedings (2024) |
| 52318 | 0.891 | Nogueira A.F.R.; Oliveira H.S.; Machado J.J.M.; Tavares J.M.R.S. | Sound Classification And Processing Of Urban Environments: A Systematic Literature Review | Sensors, 22, 22 (2022) |
| 14305 | 0.89 | Agarwal M.; Gill K.S.; Chattopadhyay S.; Singh M. | Classification Of Urban Sound Using Sequential Convolutional Neural Network (Cnn) Model And Its Visualisation | 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024 (2024) |
| 59656 | 0.89 | Hidayat A.; Njoo D.B.P.; Adrian G.D.; Setyoko D.E.; Wijanarko B.D. | Unlocking Soundscapes: Harnessing Machine Learning For Sound Classification | Proceeding of 2024 9th International Conference on Information Technology and Digital Applications, ICITDA 2024 (2024) |
| 3614 | 0.886 | Mohmmad S.; Sanampudi S.K. | A Parametric Survey On Polyphonic Sound Event Detection And Localization | Multimedia Tools and Applications, 84, 20 (2025) |
| 50556 | 0.885 | Shabbir A.; Cheema A.N.; Ullah I.; Almanjahie I.M.; Alshahrani F. | Smart City Traffic Management: Acoustic-Based Vehicle Detection Using Stacking-Based Ensemble Deep Learning Approach | IEEE Access, 12 (2024) |
| 60186 | 0.882 | Agarwal M.; Gill K.S.; Aggarwal P.; Rawat R.S.; Sunil G. | Urban Sound Classification Using Vgg19 Convolutional Neural Network (Cnn) Model And Its Visualisation | 4th International Conference on Innovative Practices in Technology and Management 2024, ICIPTM 2024 (2024) |
| 44289 | 0.881 | Saradopoulos I.; Potamitis I.; Ntalampiras S.; Rigakis I.; Manifavas C.; Konstantaras A. | Real-Time Acoustic Detection Of Critical Incidents In Smart Cities Using Artificial Intelligence And Edge Networks | Sensors, 25, 8 (2025) |