Toward Inclusive Smart Cities: Sound-Based Vehicle Diagnostics, Emergency Signal Recognition, and Beyond

Machines, Journal Year: 2025, Volume and Issue: 13(4), P. 258 - 258

Published: March 21, 2025

Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) smart cities. Despite the clear necessity sound-based diagnostic systems, scarcity of specialized publicly available datasets presents major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes significance diagnostics real-time faults through analyzing sounds directly generated vehicles, such as engine or brake noises, and classification external emergency sounds, like sirens, relevant to vehicle safety. Secondly, paper introduces novel dataset encompassing environmental noises specifically curated address absence datasets. A comprehensive framework proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, Chromatograms), using 11 models. Evaluations both compact (52 features) expanded (126 representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar Universal Joint Failure, Knocking, Pre-ignition Problem remain challenging. Logistic Regression yielded highest accuracy 86.5% (DB1) features, while neural networks performed best DB2 DB3, achieving 88.4% 85.5%, respectively. In second scenario, Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach significantly enhancing 91.04% DB1, 88.85% DB2, 86.85% DB3. These results highlight effectiveness proposed methods addressing key ITS limitations accessibility individuals disabilities auditory-based recognition systems.

Language: Английский

Hybrid feature selection-based machine learning methods for thermal preference prediction in diverse seasons and building environments

Building and Environment, Journal Year: 2024, Volume and Issue: 269, P. 112450 - 112450

Published: Dec. 11, 2024

Language: Английский

Star: semi-supervised tripartite attribute reduction

International Journal of Machine Learning and Cybernetics, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 7, 2024

Language: Английский