NEXT-GENERATION SMART CITIES: BREAK THROUGHS IN EDGE-BASED, PRIVACY-PRESERVING SOUND EVENT DETECTION FRAMEWORKS FOR URBAN AUDIO PERCEPTION

Abstract

The rapid proliferation of Internet of Things (IoT) sensor networks and ambient intelligence platforms has positioned acoustic monitoring as a critical sensory modality for intelligent urban governance. Sound Event Detection (SED)—the computational process of identifying, classifying, and temporally localizing discrete auditory occurrences within continuous audio streams—has emerged as a foundational technology for enabling situational awareness in metropolitan environments. This paper presents a comprehensive analytical framework examining the contemporary landscape of SED methodologies, with particular emphasis on their deployment viability within smart city infrastructures. We systematically investigate the evolutionary trajectory of detection architectures, spanning from classical feature- engineered pipelines utilizing Mel-Frequency Cepstral Coefficients (MFCCs) and Gaussian Mixture Models (GMMs) to modern deep learning paradigms incorporating Convolutional Recurrent Neural Networks (CRNNs), Audio Spectrogram Transformers (AST), and self-supervised foundation models such as BEATs and wav2vec 2.0. A critical comparative analysis of publicly available benchmark datasets—including UrbanSound8K, DESED, SONYC-UST, and FSD50K—is conducted, evaluating their representativeness for polyphonic urban acoustic scenes. Furthermore, this work examines emerging paradigms in edge-optimized inference, federated learning for distributed acoustic monitoring, and privacy-preserving feature extraction techniques essential for socially responsible deployment. Experimental evidence from recent Detection and Classification of Acoustic Scenes and Events (DCASE)challenge iterations demonstrates that transformer-based architectures augmented with self-supervised pretraining achieve Polyphonic Sound Detection Scores (PSDS) exceeding 0.75, representing substantial improvements over conventional approaches. The paper concludes by identifying critical research gaps and proposing a unified architectural framework for resilient, privacy-compliant urban acoustic intelligence systems.