Paper Title

Pocket Music Master

Article Identifiers

Registration ID: IJNRD_220425

Published ID: IJNRD2405791

DOI: Click Here to Get

Authors

Navaneeth T , Sivapprakash C , Surya Narayanan H , Vaitheeswaran R , suchithra C

Keywords

Data collection, Annotation, Preprocessing, Model architecture, Collaboration with psychology experts, Language preferences, Continuous monitoring, Iterative refinement, Mental health support, Technology and well-being.

Abstract

The "Pocket Music Master" is a groundbreaking AI system redefining musical engagement and vocal development. By leveraging advanced machine learning and cutting-edge audio analysis, it surpasses traditional pitch identification, discerning both pitch and nuanced tonal qualities in users' singing, even distinguishing between major and minor tonalities. Powered by a robust machine learning framework trained on diverse musical notes and vocal performances, the system provides real-time auditory experiences, accurately predicting and interpreting user-sung notes.What sets this application apart is its immersive feedback mechanism. As users sing, the system synthesizes predicted notes using virtual instrument technology, harmonizing seamlessly with the user's voice. This immediate, interactive feedback loop serves as a powerful motivator for users to refine their vocal skills with each performance. Beyond vocal practice, the application becomes an essential tool for music education, offering instant note recognition and playback. The "Pocket Music Master" represents a fusion of AI, machine learning, and audio analysis, creating a transformative platform for musical exploration, education, and self-improvement, all condensed into a portable and accessible experience.

How To Cite (APA)

Navaneeth T, Sivapprakash C, Surya Narayanan H, Vaitheeswaran R, & suchithra C (May-2024). Pocket Music Master. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), h690-h695. https://ijnrd.org/papers/IJNRD2405791.pdf

Issue

Volume 9 Issue 5, May-2024

Pages : h690-h695

Other Publication Details

Paper Reg. ID: IJNRD_220425

Published Paper Id: IJNRD2405791

Downloads: 000121975

Research Area: Engineering

Country: Coimbatore, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405791.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405791

About Publisher

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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Call For Paper

Call For Paper - Volume 10 | Issue 10 | October 2025

IJNRD is a Scholarly Open Access, Peer-reviewed, and Refereed Journal with a High Impact Factor of 8.76 (calculated by Google Scholar & Semantic Scholar | AI-Powered Research Tool). It is a Multidisciplinary, Monthly, Low-Cost Journal that follows UGC CARE 2025 Peer-Reviewed Journal Policy norms, Scopus journal standards, and Transparent Peer Review practices to ensure quality and credibility. IJNRD provides indexing in all major databases & metadata repositories, a citation generator, and Digital Object Identifier (DOI) for every published article with full open-access visibility.

The INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to advance applied, theoretical, and experimental research across diverse fields. Its goal is to promote global scientific information exchange among researchers, developers, engineers, academicians, and practitioners. IJNRD serves as a platform where educators and professionals can share research evidence, models of best practice, and innovative ideas, contributing to academic growth and industry relevance.

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Important Dates for Current issue

Paper Submission Open For: October 2025

Current Issue: Volume 10 | Issue 10 | October 2025

Impact Factor: 8.76

Last Date for Paper Submission: Till 31-Oct-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

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Subject Category: Research Area

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