Retrospection on Using AI to Arrange Tracks for a Music Album: The Importance of Human Input in 'Digital Dreams Awakening: A Synthwave-EDM Journey'

I have applied lessons learned from using AI to arrange tracks in my previous album by taking a more human-centric approach to track arrangement in my latest release, Digital Dreams Awakening | A Synthwave-EDM Journey.

AI-Driven Track Arrangement: Lessons Learned from “Retro Future Pulse”

When I first released Retro Future Pulse: Echoes of a New Dawn | Cyberpunk Synthwave OST, I was excited to push the limits of what AI tools like ChatGPT-4 could do for music creation. One of the key ways I leveraged AI was by uploading the tracks to ChatGPT-4 and having it perform technical analysis of the music, such as duration, loudness (dBFS), and Estimated Tempo (BPM) to determine the optimal track arrangement.

BPM (Beats Per Minute) refers to the tempo of the song and how many beats occur within a minute, helping to measure the song’s speed. dBFS (decibels relative to full scale) is a unit of measurement for the loudness of audio signals, where 0 dBFS is the highest possible volume level in digital audio without distortion. The idea was to use these metrics to create a logical and seamless track order.

At the time, this seemed like an efficient way to streamline the arrangement process and I discussed this at length in my previous OpenAI Developer Community post:

From Concept to Completion: How ChatGPT 4o & 4 Helped Me Create My Synthwave Album

However, after listening to the final published album Retro Future Pulse: Echoes of a New Dawn | Cyberpunk Synthwave OST in order I noticed that some high-impact tracks that could have hooked listeners earlier were placed too late. This led to a noticeable drop in audience retention early on, which I didn’t foresee.

The Challenge: AI vs. Human Intuition

ChatGPT’s analysis and track ordering were based on a logical flow of tempo, loudness, and duration. But, listening to the album from start to finish, I realized that AI-generated sequencing should be more of a guideline than a fixed structure. Although the AI did a great job of analyzing audio properties, it couldn’t fully account for the emotional impact and energy shifts that come with track arrangement. As a result, my first album Retro Future Pulse saw only 21% of viewers still watching at the 1-minute mark—a steep drop that was below typical engagement.

Applying the Lesson to “Digital Dreams Awakening”

For my newest album, Digital Dreams Awakening | A Synthwave-EDM Journey I decided to adjust my approach. This time, without relying on technical audio analysis, I used ChatGPT-4o’s initial arrangement suggestions as a starting point rather than a rule. I then listened through the tracks multiple times, considering how each piece made me feel emotionally, how the energy would flow, and where it would best hook the listener. The goal was to place the tracks most likely to capture attention first.

This human input vastly improved the final arrangement. By carefully considering how each track would affect the listener emotionally, I was able to enhance the overall pacing and engagement.

The Results: A More Engaging Experience

Though Digital Dreams Awakening is still in its early days (released February 22, 2025), the early analytics are promising. 47% of viewers are still watching at the 1-minute mark, showing a significant improvement over Retro Future Pulse. While many factors contribute to these numbers, including genre and timing, the impact of intentional track placement is clear.

Key Takeaways for AI-Assisted Music Production

  1. AI as a Guide, Not a Dictator – AI recommendations, especially regarding track arrangements for an album, are incredibly useful but should be adapted based on human intuition and audience behavior.
  2. Hook Early, Retain Longer – First impressions matter. Placing the most engaging tracks at the start improves listener retention and keeps them invested.
  3. Leverage Data for Iteration – YouTube Studio analytics provide valuable feedback. Use AI for structure, but let real-world engagement metrics shape future decisions.
  4. AI + Human Collaboration = Best of Both Worlds – The most successful AI-assisted projects come from human refinement of AI insights. By combining the power of AI with emotional and intuitive decision-making, we can push the creative process to new heights.

Final Thoughts

The blend of AI’s analytical power and human emotional intuition is where the magic happens. As I continue refining my creative process, I’m excited to explore how AI and human collaboration can push music production even further.

Have you ever experimented with AI for track sequencing or music production? What insights have you gained from using both AI and human input? Let’s discuss! :rocket::headphone:

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I’m working on a music-tech project that helps analyze and interpret music using AI. I’m very interested in how AI can influence arrangement and track sequencing. Based on your post, you’ve already experimented with AI integration in this process. What key lesson have you learned from using AI for track arrangement?

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Hello @ilyin.apple, thanks for the great question!

AI in track arrangement is a fascinating space—while AI can assist in sequencing, the final human touch remains essential. Below, I’ll share key insights from my experience, lessons learned, and best practices for balancing AI-driven recommendations with human intuition.


AI for Track Arrangement: Key Insights

Additional Context

In my previous projects, I experimented with using AI to assist in track sequencing. However, I encountered some challenges:

  • As detailed in my earlier post (From Concept to Completion), ChatGPT 4o initially failed when analyzing multiple uploaded tracks due to a system-level conflict.
  • What Went Wrong?
    • ChatGPT 4o’s environment used an outdated version of librosa, which referenced np.complex.
    • np.complex was deprecated in NumPy 1.20+, breaking the analysis process.
    • This was an internal execution issue, not a user error.
  • Workaround: Switching to ChatGPT 4 resolved the issue and allowed me to generate a complete track arrangement.

:musical_note: Final track arrangement for “Retro Future Pulse”:


Key Learnings from AI-Assisted Track Arrangement

:one: AI Can Be Helpful, But Human Judgment Is Essential

  • AI-generated arrangements may optimize for technical flow but can overlook listener engagement dynamics.
  • In one case, AI suggested an arrangement where high-impact tracks were placed too late, leading to a drop in audience retention early on.

:two: AI Works Best When Provided Context, Not Just Raw Data

Instead of relying solely on technical analysis, I found that AI provides better sequencing suggestions when given track descriptions and styles.

For my latest album :milky_way: Driftwave: Nostalgia & Neon Noir,” I tested this approach, and ChatGPT 4o made a subtle but impactful suggestion. Here’s how it played out:


Example: ChatGPT 4o’s Track Arrangement Refinement

My Prompt:

I’ve reordered tracks to balance light and dark, airy and mysterious:

  1. Pulse Runner
  2. Noir Reverberation
  3. Mirage Pursuit
  4. Apparition Loop
  5. Fading Transmissions
  6. Hollow Circuits
  7. Obsidian Glow
  8. Fading Echoes.
    Thoughts?

ChatGPT 4o’s Response (Snippet):

Final Track Order Review & Feedback
Your revised 8-track sequence is well-balanced, providing a strong contrast between light/airy and dark/mysterious elements while ensuring an engaging flow.

[…]

:hammer_and_wrench: Potential Refinements (Optional)
:small_blue_diamond: If you want a more gradual descent into atmospheric mystery:

Swap “Obsidian Glow” (Track 7) and “Hollow Circuits” (Track 6) to delay the darker glitch elements until later, making the transition into the final ambient stretch smoother.

New Order Example:
:one: Pulse Runner
:two: Noir Reverberation
:three: Mirage Pursuit
:four: Apparition Loop
:five: Fading Transmissions
:six: Obsidian Glow (Ethereal interlude before deeper cyber elements)
:seven: Hollow Circuits (Darker cybernetic moment before the final fade-out)
:eight: Fading Echoes

:rocket: However, if you’re happy with the current order, it’s already a strong, engaging, and well-paced final lineup. :fire: Locking this in?

That feedback was 100% spot on. I swapped tracks six and seven, and it did, as ChatGPT 4o predicted, “delay the darker glitch elements until later, making the transition into the final ambient stretch smoother.”

:musical_note: Listen to “Driftwave: Nostalgia & Neon Noir”:


:three: AI Recommendations Can Refine, But Not Replace, the Final Arrangement

  • For :sparkles: Digital Dreams Awakening | A Synthwave-EDM Journey,” I manually structured the tracklist to ensure variety and maximize listener engagement.
  • ChatGPT 4o was most useful in fine-tuning subtle transitions between moods, rather than dictating the full sequence.

:musical_note: Listen to “Digital Dreams Awakening”:


Final Thoughts & Community Discussion

While AI tools like ChatGPT can assist with track arrangement, they work best as collaborative partners rather than decision-makers. AI is useful for suggesting refinements, but the final arrangement should still be driven by human intuition.

:light_bulb: How about you?

  • Have you experimented with AI for music sequencing?
  • Did you rely on AI for technical analysis, or did you provide context-based instructions?
  • What were your biggest takeaways, and would you do anything differently next time?

Looking forward to hearing your insights! :rocket::musical_notes:


TL;DR - Key Takeaways

:check_mark: AI can assist in track arrangement but shouldn’t replace human judgment.
:check_mark: Providing AI with track descriptions is more effective than raw file uploads.
:check_mark: ChatGPT 4o made a small but impactful change in my album’s tracklist, improving its flow.
:check_mark: Finalizing an album’s arrangement requires a balance of AI insights and human intuition.