• A.vaishnavi
• P.Madhavi

Audio classification is a critical task in various domains, including speech recognition, music genre identification,and environmental sound detection.This study explores the use of Random Forest and Decision Tree classifiers for audio classification. These models are chosen for their simplicity, interpretability, and effectiveness in handling structured data. The process begins with data collection and labeling,followed by preprocessing steps such as normalization and noise reduction. Feature extraction techniques including the calculation of Mel-Frequency Cepstral Coefficients (MFCCs) and chroma features,are employed to convert audio signals into numerical representations.The Decision Tree classifier builds a model by recursively splitting the data based on feature values to create a tree like structure.In contrast, the Random Forest classifier constructs multiple decision trees and merges.

spectral analysis,Frequency domain,spectrogram,log-Mel-spectrogram

"Audio classification using Machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.451-456, June-2024, Available :http://www.jetir.org/papers/JETIRGL06075.pdf

"Audio classification using Machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp451-456, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06075.pdf