• Systematic review of 373 Mel-based AEDC studies from 2020 to 2025. • Five Mel feature types analysed across four key parameters. • Jensen-Shannon divergence quantifies temporal parameter trends. • Domain-specific parameter patterns across four application areas. • Practical guidance for Mel parameter selection in AEDC systems. Acoustic Event Detection and Classification (AEDC) systems are artificial intelligence technologies widely used in modern audio-based monitoring and decision-support applications. These systems may operate independently or function as perceptual modules within intelligent decision-making frameworks and expert systems. AEDC has been applied in diverse domains, including security monitoring, healthcare diagnostics, environmental surveillance, and industrial maintenance. Although Mel-based features are extensively employed in both traditional machine learning and deep learning AEDC approaches, no comprehensive review has systematically analysed their usage patterns and parameter configurations. This systematic review examines 373 AEDC studies published between 2020 and 2025 that employ Mel-based feature extraction. Five principal feature types are analysed: Log Mel Spectrogram (237 studies), Mel Spectrogram (61 studies), Log Mel Band Energies (34 studies), Mel-Frequency Cepstral Coefficients (36 studies), and Mel Band Energies (5 studies). Four critical Mel-related parameters—number of Mel filters, frame length, overlap percentage, and window type—are evaluated to identify dominant configurations, feature-specific patterns, and temporal trends. Classification architectures are systematically mapped across studies to analyse the relationship between feature selection and model design. The results reveal substantial variability in parameter choices across feature types and application contexts, as well as dominant trends aligned with deep learning frameworks. The review further identifies methodological gaps and outlines research directions to improve the design, reporting, and optimisation of Mel-based AEDC systems.

Urban vehicle accidents highlight the need for intelligent systems that can process environmental acoustic signals and improve driver awareness. Traditional acoustic event detection and classification (AEDC) methods often struggle to perform effectively when multiple acoustic events overlap in frequency. This study presents a two-stage framework for detecting and classifying horn signals in urban driving scenarios. The first stage separates horn signals from background noise using Log-Mel spectrogram features and a bagging ensemble classifier. The second stage classifies detected horns into four categories (boat, car, train, truck) using VGGish features. Feature dimensionality is reduced by approximately 40% through minimum Redundancy Maximum Relevance (mRMR) selection without losing accuracy. A post-processing step based on the binomial probability distribution corrects errors across consecutive frames in both stages. The proposed system achieves a minimum F1 score of 0.82 before post-processing and 0.96 after post-processing across all horn classes. This represents more than a 15% improvement over recent deep learning approaches, while maintaining low computational demands through feature selection. The hybrid architecture makes AEDC practical for driver assistance systems in automotive environments with limited processing resources.

Abstract The detection of sound events has become increasingly important due to the development of signal processing methods, social media, and the need for automatic labeling methods in applications such as smart cities, navigation, and security systems. For example, in such applications, it is often important to detect sound events at different levels, such as the presence or absence of an event in the segment, or to specify the beginning and end of the sound event and its duration. This study proposes a method to reduce the feature dimensions of a Sound Event Detection (SED) system while maintaining the system’s efficiency. The proposed method, using Empirical Mode Decomposition (EMD), Intrinsic Mode Functions (IMFs), and extraction of locally regulated features from different IMFs of the signal, shows a promising performance relative to the conventional features of SED systems. In addition, the feature dimensions of the proposed method are much smaller than those of conventional methods. To prove the effectiveness of the proposed features in SED tasks, two segment-based approaches for event detection and sound activity detection were implemented using the suggested features, and their effectiveness was confirmed. Simulation results on the URBAN SED dataset showed that the proposed approach reduces the number of input features by more than 99% compared with state-of-the-art methods while maintaining accuracy. According to the obtained results, the proposed method is quite promising.

Abstract Since digital media has become increasingly popular, video processing has expanded in recent years. Video processing systems require high levels of processing, which is one of the challenges in this field. Various approaches, such as hardware upgrades, algorithmic optimizations, and removing unnecessary information, have been suggested to solve this problem. This study proposes a video saliency map based method that identifies the critical parts of the video and improves the system’s overall performance. Using an image registration algorithm, the proposed method first removes the camera’s motion. Subsequently, each video frame’s color, edge, and gradient information are used to obtain a spatial saliency map. Combining spatial saliency with motion information derived from optical flow and color-based segmentation can produce a saliency map containing both motion and spatial data. A nonlinear function is suggested to properly combine the temporal and spatial saliency maps, which was optimized using a multi-objective genetic algorithm. The proposed saliency map method was added as a preprocessing step in several Human Action Recognition (HAR) systems based on deep learning, and its performance was evaluated. Furthermore, the proposed method was compared with similar methods based on saliency maps, and the superiority of the proposed method was confirmed. The results show that the proposed method can improve HAR efficiency by up to 6.5% relative to HAR methods with no preprocessing step and 3.9% compared to the HAR method containing a temporal saliency map.