The increasing need for real-time environmental monitoring has made Artificial Intelligence (AI) and Machine Learning (ML) models for audio classification and multimodal sensing essential for detecting and analyzing pollution-related sounds. In cases where mobile devices are used for capturing and processing audiovisual content, such models offer significant potential for research, storytelling, and public awareness. This study proposes a scalable and modular architecture that enables direct and flexible access to AI models for multimodal and audio processing. A CNN-LSTM hybrid model is trained for real-time environmental sound classification and deployed as a service. Building on prior 1D CNN-based approaches and incorporating temporal dependencies, the new model achieves an AUC of 0.91, demonstrating improved accuracy and generalization. The system leverages a REST API and Docker-based containerization, allowing deployment of independent AI services and supporting mobile and IoT use cases. The architecture accommodates both pre-trained and custom models, accessible from any device via a unified interface, confirming that a hybrid CNN-LSTM topology can support effective real-time sound classification, within a modular, containerized framework.
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https://aes2.org/publications/elibrary-page/?id=23002
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