AN AI-DRIVEN APPROACH FOR COMPREHENSIVE SONG FEEDBACK: FROM AUDIO PROCESSING TO QUALITY ENHANCEMENT

This paper presents a comprehensive approach to developing an AI algorithm designed to provide detailed feedback on input songs. The methodology comprises several key stages: preprocessing, feature extraction, analysis, feedback generation, and output. Initially, the preprocessing phase involves loading the audio, converting it to mono, and resampling to ensure uniformity. Feature extraction follows, capturing time-domain features such as tempo and beats, frequency-domain features including spectral centroid and Mel-frequency cepstral coefficients (MFCCs), rhythm patterns, and harmonic content via chroma features. Subsequent analysis utilizes emotion recognition, genre classification, and quality assessment techniques to evaluate the song’s characteristics. The feedback generation phase involves comparing the extracted features with reference data, identifying the song’s strengths, and suggesting improvements. Finally, the feedback is formatted and recommendations are provided in a structured manner to assist in enhancing the song’s quality.