Machine Learning Framework for Audio-Based Content Evaluation using MFCC, Chroma, Spectral Contrast, and Temporal Feature Engineering
This work addresses the problem of automated audio content evaluation for media analysis applications, but it is incremental as it builds on existing feature extraction methods.
This study tackled the problem of predicting sentiment scores for audio content by developing a machine learning framework that extracts features like MFCC and Chroma, achieving RMSE values as low as 2.783 on a 0-100 scale and showing improvements over a baseline.
This study presents a machine learning framework for assessing similarity between audio content and predicting sentiment score. We construct a dataset containing audio samples from music covers on YouTube along with the audio of the original song, and sentiment scores derived from user comments, serving as proxy labels for content quality. Our approach involves extensive pre-processing, segmenting audio signals into 30-second windows, and extracting high-dimensional feature representations through Mel-Frequency Cepstral Coefficients (MFCC), Chroma, Spectral Contrast, and Temporal characteristics. Leveraging these features, we train regression models to predict sentiment scores on a 0-100 scale, achieving root mean square error (RMSE) values of 3.420, 5.482, 2.783, and 4.212, respectively. Improvements over a baseline model based on absolute difference metrics are observed. These results demonstrate the potential of machine learning to capture sentiment and similarity in audio, offering an adaptable framework for AI applications in media analysis.