

AI Detection Report | 2024 International Songwriting Competition (Print Edition)
Evidence of undisclosed and undetected AI-generated songs receiving awards despite prohibition, undermining fair competition for songwriters
Revised Print Edition | February 2026
Author: Joseph Stanek
Date: February 1, 2026
This page hosts the revised print edition of the AI Detection Report examining the presence of AI-generated songs among award-winning entries in the 2024 International Songwriting Competition.
The report documents technical, procedural, and ethical findings based on publicly available evidence and independent expert analysis.
This report is permanently archived and citable via Zenodo (DOI):
https://doi.org/10.5281/zenodo.18465961
AI Detection Report | 2024 International Songwriting Competition (Print Edition)
This is the revised print edition of the AI Detection Report examining AI-generated songs in the 2024 International Songwriting Competition.
Abstract
This report presents an independent forensic analysis of award-winning songs submitted to the 2024 International Songwriting Competition (ISC), examining whether entries complied with the competition’s stated prohibition on AI-generated music.
Using a multi-layered audio forensic methodology incorporating waveform analysis, spectral behavior, model replication testing, and cross-platform AI detection tools, this study identifies evidence that multiple awarded songs exhibit characteristics consistent with AI generation despite formal rules prohibiting such use.
Findings raise concerns about the effectiveness of current AI detection protocols in competitive music environments and highlight structural vulnerabilities in authorship verification processes used by major songwriting contests.
This work contributes to emerging scholarship in music technology ethics, creative integrity, and audio forensics, and provides a replicable analytical framework for future investigations into AI-generated creative content.
Key Findings
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Multiple awarded songs display acoustic and structural patterns consistent with AI-generated audio models
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Certain entries demonstrate metadata and waveform behaviors not typical of human-performed recordings
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Existing detection tools failed to flag several songs later identified through manual forensic review
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Replication testing shows that stylistically similar outputs can be reproduced using contemporary generative music systems
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Current competition safeguards are insufficient to reliably enforce non-AI submission rules
Methodology Overview
This investigation employed a layered forensic approach combining:
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Spectral and waveform analysis
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Dynamic range and micro-timing evaluation
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Model replication testing using contemporary generative music systems
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Cross-tool comparison between AI detection platforms
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Manual auditory pattern recognition
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Metadata and production artifact examination
Rather than relying on a single classifier or detection score, this study used convergence across independent indicators to assess probability of AI involvement.
All findings were documented using repeatable procedures designed to support third-party verification.
Scope and Limitations
This report does not claim to establish absolute proof of AI authorship in any specific entry.
Instead, it documents convergent evidence patterns that strongly suggest AI-assisted or AI-generated origins inconsistent with competition rules.
Results reflect available tools and knowledge at time of publication.
Detection reliability is expected to evolve alongside generative model development.
Author's Statement
I undertook this investigation in response to a professional concern I have encountered with increasing frequency in my work as a musician, educator, and vocal coach: the growing tendency for individuals to claim authorship of “songs” that were generated primarily or entirely through automated AI text-to-music systems. In recent years, I have observed a rising number of students arrive with what they describe as “original compositions,” only to later disclose that their role in the creative process consisted entirely of entering prompts into generative AI platforms.
As an artist who has devoted more than three decades to the study and practice of composition, lyric writing, vocal technique, and musical interpretation, I find this trend deeply troubling. The term “songwriter” appears to be undergoing a quiet redefinition—one that displaces discipline, craft, and musical judgment with automation, without serious scrutiny or resistance.
Approximately one year prior to initiating this investigation, one of my students asked for assistance in writing and submitting a song to the International Songwriting Competition (ISC). That request introduced me to the competition. I agreed to help, intending to guide her through the full human process of songwriting from the ground up: lyrical development, melodic construction, structural planning, and vocal performance. As she confronted the scope of that work, she ultimately withdrew. Her response crystallized the concern I had already begun to feel: many now equate AI generation with creative authorship, and the distinction between the two is rapidly eroding.
In response, I chose to write and submit a song myself—completed entirely within the week preceding the ISC’s Extended Deadline submission cut-off date—not with the intention of taking home the grand prize, but as a pedagogical exercise for my students to bear witness to and as a personal and professional reaffirmation of human creative processes. I documented that work, submitted it, and then moved on.
Nearly a year later, I encountered an ISC promotional message and revisited the competition website to listen to the prior year’s winners. The second-place entry in the Comedy category—the same category I had entered—immediately exhibited auditory characteristics inconsistent with human vocal and compositional production. The experience was disquieting. The very institution I had used to model artistic discipline now appeared to reward precisely the shortcut I sought to discourage.
That moment became the catalyst for this investigation.
I did not initiate this inquiry to challenge artistic merit, to advance my own submission, or to pursue any sort of recognition as a songwriter. I include my status as a prior entrant solely as a matter of transparency. My placement is irrelevant to the findings, the methodology, and the conclusions of this report. I neither expect nor seek reconsideration of my own entry, and no aspect of the analysis depends upon it.
This investigation exists because honesty and human creativity is worth protecting—not sentimentally, but structurally. Songwriters deserve a creative ecosystem in which authorship retains meaning. They also deserve competitions that enforce their own rules. Institutions like the International Songwriting Competition that claim to celebrate human artistry must do so with procedural integrity.
My purpose in conducting and publishing this work has been consistent throughout:
to defend the value of human musical authorship,
to expose procedural weaknesses where they exist,
and to speak plainly at a moment when technological convenience threatens to outpace ethical clarity.
This report is offered in service of those values.
Conflict of Interest Disclosure
The author discloses that he was an entrant in the 2024 International Songwriting Competition. This participation is disclosed solely in the interest of transparency.
The author’s own submission was not selected for advancement or recognition, and no aspect of this investigation concerns the author’s entry, placement, or eligibility. The methodologies, findings, and conclusions presented in this report are independent of the author’s participation and do not rely on comparative evaluation against his own work.
The author has no financial relationship with the International Songwriting Competition, its judges, or its administrative staff, and has received no compensation or benefit from conducting or publishing this investigation.
This inquiry was undertaken in a personal and professional capacity as a musician and educator concerned with the preservation of human authorship standards in creative competitions.
About the Author
Joseph Stanek is a musician, producer, educator, and scholar whose career spans more than three decades of focused study in vocal acoustics, performance science, and artistic interpretation. His work centers on the development of technically grounded, psychologically sustainable, and authentically expressive musical performance.
Stanek has served as a vocal coach and creative collaborator for internationally recognized artists, including Kristin Chenoweth, Jennifer Hudson, Andrea Bocelli, and Ariana Grande. His contributions have supported multiple Billboard-charting musical albums, including Chenoweth’s album The Art of Elegance, which debuted at No. 1 on Billboard’s Top Jazz Albums chart and remained in the top position for eight consecutive weeks.
His production work includes large-scale broadcast projects such as the Tabernacle Choir’s internationally televised Christmas concert Angels Among Us, which reached more than 66 million households worldwide.
A recipient of the Pi Kappa Lambda Scholarly Writing Award, Stanek founded Tour de Fierce® as a platform for cultivating honest, skill-driven performance practices grounded in physiological awareness and artistic integrity. His work emphasizes the responsible integration of emerging technologies—including artificial intelligence—within transparent, human-centered creative frameworks.
His professional mission is to preserve the meaning of authorship in an era of automation while supporting artists in developing sustainable, verifiable, and expressive creative voices.
9. Contact
Joseph Stanek (Seph Stanek)
Producer | Researcher | Educator
Founder & Owner of Tour de Fierce®
New York, NY
Email: contact@tourdefierce.vip
Full Report:
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© 2026 Joseph Stanek. All rights reserved.
Portions of this report may be quoted or referenced with proper attribution.
Please link to the official DOI page when citing this work.
Reproduction or distribution of the full report requires written permission from the author.
Cite This Report
Recommended Citation:
Stanek, J. (2026). AI Detection Report: 2024 International Songwriting Competition (Print Edition). Tour de Fierce Research.
DOI: https://doi.org/10.5281/zenodo.18465961
What's Next
Additional reporting and analysis exploring how institutional decisions shape artistic legacy, authorship, and public trust.

