Performance Evaluation of Environmental Sound Classification: A Machine Learning Stacking and Multi-Criteria Metrics Based Approach
This study proposes an Environment Sound Classification Task (ESC) model that includes numerous element
channels given as a contribution to Machine learning with an Attention instrument. ESC is a significant testing
issue. The interest in the paper lies in utilizing different part channels involving the MFCCs-Mel Frequency
Cepstral Coefficients a mutual module in speaker detection and artificial speech systems. LPCs-Linear Prediction
Coefficients and Linear Prediction Cepstral Coefficients were the most commonly used types in ASR- Automated
speech recognition. The paper also discusses some basic features of MFCCs and how to put them into practice.
The techniques used for this project are Background Gaussian Noise and Time Shifting, by observing that every
technique is implemented with a provided probability, however, when at the time of generation of a new sample the same spectrogram input may have several combinations. We go through the blend information expansion method to additional lift execution. Our model can accomplish cutting-edge execution on three benchmark climate sound characterization datasets, for example, the UrbanSound8K.
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