International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

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

Author(s) Ms A S Aarthi Mai, Ms N Rajeswari
Country India
Abstract 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.
Keywords AI Algorithm, Audio Processing, Feature Extraction, Emotion Recognition, Genre Classification, Quality Assessment, Feedback Generation
Field Computer Applications
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-02
Cite This AN AI-DRIVEN APPROACH FOR COMPREHENSIVE SONG FEEDBACK: FROM AUDIO PROCESSING TO QUALITY ENHANCEMENT - Ms A S Aarthi Mai, Ms N Rajeswari - IJSAT Volume 16, Issue 2, April-June 2025. DOI 10.71097/IJSAT.v16.i2.4480
DOI https://doi.org/10.71097/IJSAT.v16.i2.4480
Short DOI https://doi.org/g9hbr2
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org