This paper presents a comprehensive review of existing methods and approaches for Sound Event Detection (SED) in environmental acoustic monitoring. The theoretical foundations of SED, acoustic feature extraction techniques (MFCC, Mel-spectrogram, DCT-spectrogram, and cochleogram), traditional machine learning methods (GMM, HMM, and SVM), and modern deep learning architectures (CNN, RNN/LSTM, CRNN, and Transformer) are comprehensively analyzed. The review shows that between 2000 and 2012, manually engineered features combined with conventional machine learning methods achieved detection accuracies of 65–75%, whereas deep learning approaches introduced after 2012 increased performance to 85–96%. In particular, Convolutional Recurrent Neural Networks (CRNNs) achieve accuracies exceeding 90% by effectively modeling local spectral patterns and temporal dependencies while benefiting from ensemble learning techniques. The paper also analyzes widely used benchmark datasets (ESC-50, UrbanSound8K, AudioSet, and FSD50K) and highlights the need for developing specialized datasets for emergency sound detection in residential environments. Furthermore, practical challenges such as noise robustness, real-time inference, and edge computing are discussed, together with promising research directions including Transformer-based architectures, self-supervised learning, and multimodal fusion. The presented review provides a valuable reference for researchers and practitioners working in the field of sound event detection.
| Mualliflar | Набиева, Д.Т., Юлдашева, У.Х. |
|---|---|
| Jurnal | Рақамли технологияларнинг назарий ва амалий масалалари |
| Nashr sanasi | 2026-08-02 |
| Jild | 9 |
| Son | 3 |
| Betlar | 53-74 |
| Til | Rus |
| DOI | 10.62132/ijdt.v9i3.398 |
DOI: 10.62132/ijdt.v9i3.398 · Maqolaning asl sahifasi
обнаружение звуковых событий, глубокие нейронные сети, CRNN, акустический мониторинг, умные города, чрезвычайные ситуации, машинное обучение, sound event detection, deep neural networks, CRNN, acoustic monitoring, smart cities, emergency situations, machine learning
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