AI and Music

A truly effective AI music tool will produce music that is not only technically sound and varied but also creatively compelling and emotionally resonant with listeners. 

The effectiveness of an AI music tool can be judged by combining subjective human listening tests (subjective assessments) with objective, technical evaluations and metrics.

Subjective criteria (Human evaluation)

This approach assesses how human users and listeners perceive the AI's output, measuring its artistic merit. 

• Creativity and novelty: Does the music sound fresh and original, or is it predictable and repetitive? An effective tool should be able to generate unique melodies and harmonies rather than simply reproducing its training data.
• Emotional resonance: Does the music evoke the intended feeling or mood? The best tools can translate a prompt for a "sad song" or an "upbeat track" into a piece that effectively communicates that emotion to a listener.
• User satisfaction: Users should find the tool easy and enjoyable to use. An effective tool often receives high ratings from users, who are satisfied with the quality of the generated music and the overall experience.
• Human-like quality: In a blind test, how often is the AI-generated music mistaken for human-created music? A high score indicates a tool is effective at mimicking human-level nuance and complexity.
• Coherence: Does the music flow logically? A coherent piece of music follows consistent melodic, harmonic, and rhythmic patterns, creating a cohesive and satisfying listening experience. 

Objective criteria (Technical analysis)

Objective evaluation provides a quantifiable, data-driven assessment that is less prone to human bias. 

• Musicality metrics: Use algorithms to analyze core musical components and compare them to musical theory standards.
• Melodic coherence: Does the melody have a clear and logical structure?
• Rhythmic precision: Is the timing of the beats and syncopation accurate and consistent?
• Harmonic accuracy: Do the chord progressions make sense within the designated key and genre?
• Diversity and complexity: These metrics measure the variety and richness of the generated music.
• Diversity: A tool's effectiveness can be judged by how varied its output is for the same or similar prompts.
• Complexity: This assesses the intricacy of the harmony, melody, and rhythm, showing the model's capacity to create sophisticated music.
• Performance metrics: Standard benchmarks can evaluate the tool's efficiency and reliability.
• Generation speed: How quickly can the tool produce music?
• Reproducibility: Does the tool consistently produce similar outputs from the same or similar prompts? 

Hybrid and Multimodal Evaluation

The most comprehensive evaluations combine both subjective and objective measures, sometimes in a "hybrid" approach. Some platforms also use a "multimodal" evaluation, where they assess how the music integrates with other media, such as video. 

• Human-in-the-loop: A robust hybrid method uses objective metrics to filter or rank the AI's output, with human users providing a final subjective rating. This can improve efficiency while retaining focus on creative quality.
• Multimodal alignment: For tools generating soundtracks for video games, films, or podcasts, effectiveness is judged by how well the music synchronizes with and enhances the accompanying visuals or narrative.
• User co-creation: For AI tools that assist in the creative process, a measure of effectiveness is how well the AI collaborates with the user and whether it successfully augments their own musical creativity. 

The very few articles listed below are just indicators of the current research relating to the assessment of AI tools in music education.  Google Scholar is a good resource for further investigations on this topic.

Cros Vila, Laura, Bob Sturm, Luca Casini, and David Dalmazzo (2025). "The AI Music Arms Race : On the Detection of AI-Generated Music", Transactions of the International Society for Music Information Retrieval,  8(1), 179-194.

Abstract

Several companies now offer platforms for users to create music at unprecedented scales by textual prompting. As the quality of this music rises, concern grows about how to differentiate AI-generated music from human-made music, with implications for content identification, copyright enforcement, and music recommendation systems. This article explores the detection of AI-generated music by assembling and studying a large dataset of music audio recordings (30,000 full tracks totaling 1,770 h, 33 m, and 31 s in duration), of which 10,000 are from the Million Song Dataset (Bertin-Mahieux et al., 2011) and 20,000 are generated and released by users of two popular AI music platforms: Suno and Udio. We build and evaluate several AI music detectors operating on Contrastive Language Audio Pretraining embeddings of the music audio, then compare them to a commercial baseline system as well as an open-source one. We applied various audio transformations to see their impacts on detector performance and found that the commercial baseline system is easily fooled by simply resampling audio to 22.05 kHz. We argue that careful consideration needs to be given to the experimental design underlying work in this area, as well as the very definition of "AI music." We release all our code at https://github.com/lcrosvila/ai-music-detection.

Liu, H., & Guo, W. (2025). "Effectiveness of AI ‐Driven Vocal Art Tools in Enhancing Student Performance and Creativity," European Journal of Education, 60(1), 1-9.

Abstract

In contemporary music education, innovative technologies, particularly artificial intelligence (AI)-based tools, play a crucial role. The objective of this study was to assess the effectiveness of AI-based tools in enhancing students' success and creativity. The study involved 158 students from a leading music institution, who were divided into control and experimental groups. Methods employed included surveys and testing, along with AI-based tools: Vocal AI Analyzer and Smart Vocal Coach. The results indicated a significant improvement in vocal skills (from 3.5 to 4.5 in the experimental group) and creativity (from 2.9 to 4.1 in the experimental group) compared with the control group. The AI-based tools demonstrated high effectiveness, providing individualised instruction and immediate feedback. The practical significance of the research lies in the potential implementation of such technologies in music educational institutions to enhance teaching effectiveness and the development of students' creative abilities.

Peng, S., & Ratnavelu, K. (2024). “Artificial intelligence-assisted Music Learning: A Systematic Review of the Risks and Crises of Higher Music Education,” Journal of International Crisis and Risk Communication Research, 7(2), 466-476. 

Abstract

Based on the rapid development of artificial intelligence (AI) technology and its increasing application in music education, we need to understand the application and development trend of AI in higher music education, as well as possible risks and crises. Supported by qualitative research methods, this study focuses on literature reviewand analysis. This study selects scientific publications to evaluate the influence of AI on all aspects of music learning, to further develop the application of AI in music education. The research in the summary shows that AI can effectively promote students' participation and investment in music learning, enhance the learning experience, increase the number of students and the efficiency of teachers, and improve the supervision, implementation, and evaluation of music courses. It is suggested to further discuss the effective curriculum design or implementation strategy of integrating AI into music education, which is very important for effectively improving the effect of music learning.

Wilson, E., Wszeborowska, A., and Bryan-Kinns, N. (2025). “A Short Review of Responsible AI Music Generation,” In Proceedings of the 6th Conference on AI Music Creativity (AIMC 2025), Brussels, Belgium, September 10th -12^th.

Abstract:

Artificial intelligence continues to become increasingly embedded in musical practice and yet there is little evaluation of how transparent and ethical these systems are. Surveys of AI models to date focus on the technical features of AI models and there is a lack of surveys of the practical and ethical application of AI models for musicians, producers, and composers. This paper surveys 27 contemporary AI models for music generation in terms of creative input and output (symbolic music, audio, text, or image), musical task (symbolic composition or audio generation), and Responsible AI properties of transparency & explainability, fairness, accountability, and ethical AI. Analysis of these facets of AI model use in creative practice highlights the trade-offs and challenges in designing equitable and ethical AI tools for music-making. The survey highlights a lack of transparency and control of AI model training and fine-tuning, a lack of openness of licensing and source code, a lack of ethical reporting of training datasets, and a focus on AI models for audio generation at the expense of real-time music generation for use in composition, performance, and improvisation. Our analysis offers insights for researchers, developers, and musicians seeking to navigate this fast evolving landscape of musical AI. We suggest that research is needed to develop clearer frameworks for evaluating AI models in creative domains, focussing especially on user journeys that help users understand the mechanics, limitations, and ethical considerations of these systems in music making practice.

Zhang, Lei (2025). “The Complementary Role of Artificial Intelligence to Traditional Teaching Methods in Music Education and Its Educational Effectiveness.” Applied Mathematics and Nonlinear Sciences, 10(1). 1-17.

Abstract

In the field of music education, the application of artificial intelligence technology is gradually changing the traditional teaching mode, providing new opportunities and challenges for music education. In this paper, we use artificial intelligence technology to build a smart classroom for music teaching and combine it with a user-based collaborative filtering recommendation algorithm to provide students with personalized music learning materials. Moreover, a treble feature extraction model is integrated into the smart classroom, and the DTW improvement algorithm is used to match the students’ treble features, and the student’s mastery of music skills in the smart classroom is evaluated through the sight-singing scoring technology. Students’ overall satisfaction ratings for the music teaching mode in the smart classroom designed in this paper were 4.35 to 4.60, and only a very few students disliked the teaching mode. The personalised recommendation system built in this paper has a precision rate, recall rate and F-value of 0.50, 0.41 and 0.38, respectively, when the number of recommendations is 50, and it can provide students with personalised music learning materials suitable for them. After the experiment, the average scores of the experimental class on pitch, rhythm, sight-reading ability, music notation, and polyphonic music perception increased by 7.72, 6.37, 7.82, 6.92, and 8.16 points, respectively, compared with the control class. In this paper, the difference between the intelligent scoring system and the teacher’s scores on the “pitch” scores is 0.036~4.903. Artificial intelligence technology provides an effective supplement to traditional music teaching and improves the personalization, efficiency, and quality of teaching.