• Anam Bansal

: Environmental Sound Classification (ESC) has emerged as a prominent research domain, aiming to categorize various real-world audio events for use in context-aware systems. Unlike speech and music, environmental sounds are inherently unstructured, making their classification more challenging. Over the years, researchers have explored a range of preprocessing methods, feature extraction techniques, and classifiers for ESC. While notable progress has been made in terms of accuracy, there remains potential for enhancement through strategic feature combinations. This study investigates the classification performance of various handcrafted features, including temporal (Root Mean Square, Zero Crossing Rate), spectral (Chroma, Spectral Flatness, Tonnetz), cepstral (Mel Frequency Cepstral Coefficients), and image-based (Log-Mel Spectrogram) features. These features were selectively combined based on insights from previous work and evaluated using five traditional machine learning classifiers. Among the tested combinations, the pairing of Mel Frequency Cepstral Coefficients (MFCCs) and Chroma features delivered the highest classification accuracy of 81.54% with the K-Nearest Neighbor (KNN) classifier. The proposed approach demonstrates superior performance compared to several existing benchmark studies, while maintaining low computational complexity

Audio classification, Audio features, Chroma, Classification accuracy, Decision Tree, Environmental Sound Classification, Feature combination

"A Comparative Study of Handcrafted Features and Classifiers for Efficient Environmental Sound Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.e422-e438, September-2025, Available :http://www.jetir.org/papers/JETIR2509448.pdf

"A Comparative Study of Handcrafted Features and Classifiers for Efficient Environmental Sound Recognition", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppe422-e438, September-2025, Available at : http://www.jetir.org/papers/JETIR2509448.pdf