Seven Days Of Testing AI Music Generation: A Creator’s Raw Log

Day one started with skepticism. 

I have tested enough AI music tools to know that most can produce a passable 30‑second loop but fall apart when you need a complete song with verses, a bridge, and an ending that does not just fade into digital silence. 

So I decided to keep a daily log of this AI music generation journey, pushing one platform through a different real‑world task each day for a full week. 

The tool I used was the AI Song Generator, and I committed to using only the features described on its site, no hidden settings or workarounds. 

What follows is that log, edited for clarity but with every success and failure left intact.

A Week Of Testing AI Music Generation: A Complete Breakdown Of The Progress

Here, I have given you a complete breakdown of the whole process. This way, you will be able to understand the actual progress.

Let’s take a look at this:

Day 1 – Generating A Complete Song From Scratch 

The Task: A Three‑Minute Pop Track with Full Lyrics 

I started with the most basic use case: a song with original lyrics, a clear structure, and a vocal performance that did not sound like a text‑to‑speech robot singing karaoke. 

I wrote a simple set of lyrics about a delayed train and fed them into the platform with a prompt for “upbeat acoustic pop, male vocal, verse‑chorus‑verse‑chorus‑bridge‑chorus.” 

The generation took 45 seconds. 

The result had proper verse‑chorus differentiation, a bridge that modulated to a higher key, and a vocal that carried emotional weight despite being clearly synthesized. 

The ending was a held chord on the acoustic guitar, not a fade. Success for day one. 

The only flaw was a slightly rushed pre‑chorus transition that felt like one beat was missing. 

Regenerating with “add one bar of instrumental before the chorus” fixed it. 

Day 2 – Isolating The Vocal From A Dense Mix 

The Task: Clean Acapella Extraction for a Remix 

Day two was about testing the vocal remover. 

I generated a new track with “dense electronic production, layered synths, heavy sidechain compression, female vocal.” 

The original vocal sat well in the mix. 

Running it through the vocal removal tool produced an acapella that preserved the vocal’s character, but low‑end synth bass bled into the stem. 

The instrumental track, however, was clean enough to use as backing. 

For a remix or sampling session, the acapella would need light high‑pass filtering. 

The platform also offers a four‑stem splitter (drums, bass, vocals, other). 

In that mode, the separation improved noticeably, with less bleed into the vocal stem. 

The trade‑off: the four‑stem splitter takes about twice as long to process. 

Day 3 – Extending A Track To Match Video Length 

The Task: Adding 30 Seconds to an Existing Instrumental 

Video editors often need music that fits a specific runtime. 

I took an instrumental track from day one that ran 2 minutes 47 seconds, and used the song extension tool to add exactly 30 seconds. 

The interface asks where to extend from (beginning, middle, or end) and how long to add. I chose “end” and “30 seconds.” 

The generated extension replicated the harmonic and rhythmic feel of the original but introduced a slight change in the drum pattern. 

For background use, the shift was barely noticeable. For a loop‑based project, the change might be distracting. 

The tool works best when the extension point is a natural cadence or a held chord. 

Extending from a busy rhythmic section produced more noticeable seams. 

Day 4 – Generating Lyrics From A Theme 

The Task: Creating Lyrics for a Niche Topic (Quantum Physics) 

I do not write lyrics regularly, so day four tested the AI’s lyric-generation. 

I entered “quantum physics, uncertainty principle, romantic metaphor.” The system returned three verse‑chorus structures. 

The lyrics were thematically coherent, using words like “superposition” and “observation” as metaphors for relationship uncertainty. 

However, the phrasing was occasionally unnatural for singing, with too many syllables in one line and too few in the next. 

Using the generated lyrics as a draft and editing for scansion worked well. 

The tool is a strong starting point, but not a finished lyricist.

Day 5 – Testing Style Consistency Across Multiple Generations 

The Task: Three Songs in the Same “Dark Synthwave” Style 

For a game soundtrack, style consistency matters. 

I ran three separate generations with identical prompts: “dark synthwave, 100 BPM, minor key, pulsing bass, arpeggiated synth leads, no vocals.” 

The three results shared the same vibe and tempo but used different chord progressions and synth patches. 

That is excellent for variety within a consistent style, but it means you cannot generate two identical tracks. 

If your project requires exact musical motifs to be repeated, you will need to rely on the extension tool or accept variations. 

For most game and video work, the variation is a feature, not a bug. 

Day 6 – Vocal Removal On A User‑Uploaded Track 

The Task: Removing Vocals from an AI‑Generated Song from Another Platform 

To test the tool’s general audio processing, I exported an AI‑generated track from a different service (not named here) and uploaded it to the vocal remover. 

The platform processed external files without complaint. 

The result was decent: the vocal was reduced significantly, but the instrumental had audible artifacts, a warbling sound on sustained synth notes. 

The same upload run through the four‑stem splitter produced cleaner results. 

The takeaway: the vocal removal tools work best on music generated within the platform, but can handle external files with acceptable quality for demo purposes. 

Day 7 – The All‑In‑One Production Workflow 

The Task: Create, Isolate, Extend, and Remix in One Session 

For the final day, I ran a full workflow: generate a song, isolate its vocal and instrumental, extend the instrumental by 60 seconds, generate a second song with a different prompt, and layer the first song’s vocal over the second song’s instrumental using external editing software. 

The entire process, excluding the external layering, took 22 minutes. 

The final layered track required no pitch or tempo matching because both generations were at the same BPM (I had specified 110 BPM in both prompts). 

That kind of workflow integration is where the platform distinguishes itself. 

You are not bouncing between different tools or worrying about format compatibility. 

A Practical Comparison Of Core Operations 

Based on the week’s log, here is how the platform’s main functions compare in terms of real‑world reliability and ease of use. 

The Honest Limitations From A Week Of Daily Use 

No week of testing is complete without acknowledging what did not work well. 

First, the platform occasionally hallucinates instruments that were not requested. 

In one generation, I asked for “only piano and cello,” and the result added soft synth pads in the background. 

Regenerating with “absolutely no synthesizers” solved it, but it shows that the system has default tendencies that require explicit negation to override. 

Second, the vocal quality, while good, has a consistent “AI timbre” that some listeners might identify immediately. 

For background AI music generation in videos, it is fine. For a standalone single meant to pass as human, it does not fool trained ears. 

Third, the platform’s performance degrades slightly during peak hours. 

Generations that took 40 seconds on a Tuesday morning took 75 seconds on a Saturday evening. 

The results were identical in quality, but the waiting adds friction. 

Fourth, the four‑stem splitter, while effective, sometimes misassigns transient sounds. 

A hand clap in the original track appeared in the “other” stem instead of the “drums” stem. 

For remixing, that means checking all stems rather than assuming the assignment is perfect.

What The AI Music Generation Log Ultimately Shows 

After seven days of pushing the AI Song Maker through different tasks, the conclusion is not that it is flawless, but that it is unusually consistent for a tool in this category. 

It fails in predictable ways that you can learn to avoid. 

It succeeds in enough cases that a creator can build a reliable production workflow around it. 

The log does not show magic. 

It shows a tool that respects the user’s intent, provides clear commercial rights, and integrates post‑processing steps that other platforms treat as afterthoughts. 

For anyone tired of gambling on AI music generation tools that work once and fail the next time, that consistency is worth more than any single impressive generation.