虚ろなる救世主の残響 · 金色の傷跡

Not sure if anyone's done this seriously but I've been thinking about it for a while.

The idea is basically: pick a lane, build a consistent artist identity, style, look, maybe generate a portrait so there's a "face", make songs in that style, build out a catalog over time, put out music videos, push it to Spotify and DistroKid, run the YouTube channel like it actually belongs to a real artist. Not a lo-fi beats faceless channel. An actual persona.

Genre matters a lot for this I think. Sad indie pop, dark pop, chill singer-songwriter stuff, AI tends to handle those pretty well. Tried doing something more like a live band sound once and it just came out feeling wrong, like a cover band that learned the song from a description of the song. Some vibes AI just can't fake yet.

The part I keep going back and forth on is whether to be transparent about it being AI or just let it exist. There are channels that disclose everything and still pull real numbers. And then there are some that don't and eventually people find out and it becomes a whole thing.

Spent about 40 minutes the other night trying to name this fictional person. That probably means I'm either actually serious or just procrastinating on something else.

Revenue-wise I genuinely don't know what realistic looks like. Ad revenue on a music channel is low, everyone says that. Streaming adds up slowly. Sync licensing is probably the play if you can actually build a coherent enough sound identity, but that takes time.

Anyone tried this? Not the lo-fi passive income version, the actual "run this like a label" version.

No promo. No links. No tracks to push today.

I just genuinely wanted to check in on the creators here.

Lately, releasing music has started to feel oddly lonely.
Like spending nights building something deeply personal… then quietly leaving it in the dark, not knowing if anyone will ever find it.

I’ve been browsing a lot of AI music spaces recently, and it feels like we’re all creating constantly — albums, worlds, identities, emotions — but most of the time, we’re surrounded more by other creators than actual listeners.

Some nights it honestly feels less like “releasing songs” and more like tossing signals into a vast ocean.

For me, I make dark cinematic alternative rock with deep baritone vocals and a heavy late-night atmosphere. I care a lot about cohesion and emotional storytelling, and I’ve realized the strange part isn’t even the low numbers anymore — it’s the silence after publishing.

That quiet moment where the song is finally out there… and the world barely moves.

So I wanted to ask honestly:

How are you all holding up mentally with your projects lately?

What keeps you going when it feels like nobody is listening yet?
Have you made peace with creating mostly for yourself?
Or are you still hoping to find the people who understand what you’re trying to make?

I don’t know. Maybe a lot of us are feeling the same thing quietly.

Just wanted to open a real conversation instead of another promo thread.

Take care of yourselves, seriously.

I’ve been thinking about this a lot while using AI music generators.

Right now, most tools (Suno, Udio, Musicful etc.) are incredibly good at producing something that sounds like a song. But we also tend to immediately treat that output as if it should already be a finished, release-ready track.

That expectation feels a bit mismatched.

In traditional music creation, a “song” goes through multiple stages—idea, structure, arrangement, performance, production, mixing, mastering. Each layer adds intent and refinement. But AI tools compress all of that into a single generation step.

So we end up in this strange middle space:

• The output is often musically convincing at first listen
• But it lacks consistent direction, identity, or long-form structure
• And yet we still judge it as if it failed to be a “real finished song”

Maybe the issue isn’t that AI music is “not good enough,” but that we’re applying a finished-product lens too early in its evolution.

It might be more accurate to think of these tools as idea generators or sketch engines, where the real creative work happens after generation - selecting, editing, re-structuring, and building meaning on top.

Otherwise, we get stuck in a loop where we either overhype raw outputs as finished art, or dismiss them because they don’t meet full production standards.

So I’m curious what others think:

Are we forcing AI music tools into a “finished product” mindset too early, instead of treating them as part of a longer creative workflow?

So I wanted to take a trip to Asia, but it's a bit too far to take the train from Europe because it would take days. So I decided to fly, but then this guy shows up and starts telling me I shouldn't do that because it's a shortcut, and if man were supposed to fly, God would have given him wings. So it's better if I take the train, or even better, travel on horseback, because only that way I'll have plenty of time to think, meet new people along the way, and experience true adventure, and what a joy that would be!

The thing is, I don't feel like doing any of these things, and if it weren't for the plane, I would never have gone to Asia, but this guy keeps bugging me and won't listen to any of my explanations: I never dreamed of traveling across the continent for weeks. I really just want to see Hong Kong and come back... And he tells me I'm a worse kind of person, that I'm taking the easy way out, and that he despises me because he'll never get on a plane, because a true traveler wouldn't do that. So I better not dare call myself a traveler, because I am not one. Well, I don't consider myself a traveler, more of a tourist at most, and I just wanted to see damn Hong Kong.

Yeah, that's exactly what anti's are doing all the time.

Hey everyone.

I’ve been thinking lately that a lot of people using AI tools for music are sitting alone with their songs because every discussion eventually turns into “AI vs real musicians,” and honestly… it gets exhausting.

I’m not some “real producer” or industry person 😄
I’m just a pianist/violinist with a music background who happens to use AI because I can write music and hear arrangements in my head — but I absolutely cannot sing the way I hear things.( terrible singer 🤪😂)

So for me AI became less “generate content” and more “finally hear the songs that used to exist only in my head.”

And I’m probably not the only one.

So I’ve been thinking about maybe creating a small cozy Telegram group for people who actually make AUTHORIAL music this way:
\- writing their own lyrics,
\- concepts,
\- melodies,
\- weird genre experiments,
\- character songs,
\- emotional/soundtrack pieces,
\- and then using AI as part of the creative workflow.

Not a “500 generated tracks a day” spam place 😄
More like a small community where people can:
\- share songs,
\- discuss ideas,
\- give feedback,
\- help each other improve arrangements/prompts/structure,
\- and just talk about music without endless hate wars.

I’m not even making the group yet 😄
I just genuinely want to know if people here would actually be interested in something like that.

DM me if interested, please:) not sure if it’s relevant

A little song for Usopp with a deep, raspy voice and a lot of swagger.

I’ve been working on a desktop app called Stem Forge Pro by DiscoramaLab

The problem I wanted to solve is something I keep running into with AI music tools: the song idea can be good, but once you export the stems and try to mix them seriously, the drums and percussion often sound weak, metallic, smeared, unstable, or just not really usable in a serious DAW session.

So instead of trying to fix everything with EQ, denoise, or transient shaping, I built a donor-based reconstruction system.

The basic idea is:

keep the original groove and timing
analyze the AI-generated drum/percussion stem
separate the main roles like kick, snare, hi-hat and percussion
repair or rebuild weak parts using cleaner donor-based source material
export cleaner raw stems that are easier to mix in a DAW

The tool can generate repaired/rebuilt outputs for different drum roles, so instead of only getting one damaged AI drum stem, you can work with more useful separated material such as kick, snare, hi-hat and percussion layers.

To be clear, the output is not meant to sound like a fully mixed, widened AI stem immediately. It is more like clean raw source material: drier, more direct, and ready for proper processing with EQ, compression, saturation, reverb, stereo work, etc.

That was intentional. I didn’t want to fake a polished mix. I wanted to give producers better source material to work with instead of trying to rescue damaged AI drum stems.

One thing I noticed during testing: it’s worth trying a few different settings on the same stem. AI stems can be very inconsistent, so sometimes the best result comes from testing a couple of variations and choosing the strongest one.

The GUI is still rough, I’ll be honest. It’s functional, but not beautiful yet. My priority was getting the engine and reconstruction quality working first. The interface can improve later.

Small note: the Mac / OSX version is not finished yet, so the first release is Windows-only for now. Mac support is on the roadmap.

For now, I’m mainly looking for feedback from people who actually export AI stems and try to finish tracks properly in a DAW.

I won’t post the link here unless the mods allow it, because I don’t want to break the self-promo rules. If anyone is genuinely interested, you can search for “Stem Forge Pro Discorama Lab” or ask me.

Do you also find drums and percussion to be one of the weakest parts of AI-generated stems?

I have some thoughts about using AI as a way to put music to texts.

When I discovered that it was possible to use AI to turn my storytelling into something more, it opened up a whole new world for me. Writing a story is one thing. Curating that story through music; testing, retesting, tweaking, shaping it until it not only aligns with the original vision but adds the emotional layer that music can bring, is something else entirely.

For me, it has meant a lot. And I think it has meant a lot for many others as well.

And regardless of what some may say: it is music. However one chooses to value it does not change that fact. It is music, just as real as any other music. The difference lies in the methods and tools used.

Not everyone plays every instrument themselves. Not everyone records in a studio. Not everyone manually programs every sound in a DAW. Different tools have always shaped music.

AI music is music, but that does not mean all concerns about AI music are invalid.

Is all AI-created music high quality? No.

Is it all music? Yes.

Is it good or bad? Well, to that I say: de gustibus non est disputandum.

Just as a disclosure, I'm not an expert on AI music or youtube, but I think for what I've been doing since December 2024, I'm doing alright. I have a 74k sub channel that gets about 2 million views a month, and a few other channels. Latest channel I made started in Dec 2025 and got monetized March 2026. It's currently at 3.5k subs and earning average of 400 a month since it got monetized. I get a lot of questions so I'm condensing everything I say each time on this post.

1. Pick a clear niche

Do not make “AI music for everyone.” Don't just post random genres for a single channel. You can pivot when starting but don't make it confusing. Private videos that are no longer in line with the direction of the channel. Rebrand the channel if you need to. If I sub to your channel I want the video I subbed on to be similar to content I'll get moving forward. Pick a specific genre, mood, listener, or use case.

Make your branding purposeful. Nobody knows you or your music and sure as shit nobody will care about a fictional artist you make. Stop pretending you have a big deal artist when you don't. Intsead, package your music, videos, and channels purposefully. Make people know what they're clicking immediately.

Examples: uplifting reggae, chill house for studying, emotional breakup pop, lo-fi rap for late nights, relaxing acoustic playlists, funny songs about everyday life, or 1-hour background music for work.

A viewer should understand your channel within seconds. You can add branding but don't make it front and center until you actually have a following. I didn't even put logos on my videos until people started reuploading my content.

Also worth noting that not all niches are equal. Some niches have stronger demand and less competition while others are the other way around. Some niches are staunchly anti-AI. Figuring out a niche you'll be happy to create music for is one of the trickiest parts of starting.

Try to mix up genres. Or use music for purposes they're not designed for. Worship hiphop? Uplifting industrial rock? Carve out a subniche to stand out. It's AI music's strength.

2. Quality beats quantity

Do not mass-upload weak songs. Bad retention can hurt your channel.

Before uploading, check for a strong hook in the first 15–30 seconds, clean lyrics with no awkward AI phrasing, clear genre identity, good mix and volume, a memorable title or chorus, and a clear use case like relaxing, driving, studying, sleeping, or working.

Rule of thumb: do not upload a song you would not replay yourself.

3. Use long-form playlists/compilations

Singles are useful for testing, but long videos often work better for music channels because people use them as background listening.

Few people who are on Youtube for music will select single song videos they don't know. They usually want a playlist or a long video that just plays continuously. Interestingly, videos that are too long will suffer from low AVD so you don't want it too long either. 45 min to 1 hour seems to be perfect.

The longer format gives people a reason to stay, replay, and use your channel as background music. In turn it gives you watch hours, and subsequently, more ad revenue.

What I usually do is create 5-10 songs, and then repeat them once or twice to get a 45m-1hr. After a while I just mix up old songs with new ones so I don't have to repeat, or create a lot of new songs.

Shorts are a waste of time for AI music. Full stop.

4. Make thumbnails simple and readable

Thumbnails will determine how many views you will get. They have to stand out from everybody else. Be bright. Be loud. Be purposeful.

Use big readable text, one clear subject or mood, a consistent style, colors that match the music, and text that is readable on phones. Use Canva. If you want to use pro, you can get cheap licenses on G2G for a couple of bucks.

A simple formula is:

Big title + mood image + clear subtitle

Example:

<Image of pretty lady chilling on a rooftop>

MIDNIGHT CHILL
1 Hour Relaxing Music

5. Keep visuals simple

Avoid realistic AI singers or fake performances unless they look excellent. Bad mouths, hands, instruments, and animation can make the channel feel cheap.

Better options include static cover art, slow zoom or pan, lyric videos, (fake) audio visualizers, looping abstract animation, and mood-based background art.

A polished static video is usually better than a bad animated one.

I personally use static images with backgrounds removed, then add looping stock videos in he background, add audio visualizers, particle effects, and dynamic text. Sometimes I get fancy and use looping AI videos via Kling. I don't need anything fancy because people who watch YT for music don't care about visuals all that much. Just make it stylish enough for them to stay for the 1st 30 seconds and then let the music hook them in.

6. Use metadata wisely

Your title, description, tags, hashtags, and category should all point to the same audience.

Update your channel tags. They are used by YouTube specially for new channels.

For the videos, put three hashtags as the first three words of the description.

Example:

#LoFi #ChillMusic #StudyMusic

Original music playlist for relaxing, working, studying, driving, or background listening.

Tracklist:
00:00 Song 1
03:42 Song 2
07:15 Song 3

Created with AI-assisted music production, human curation, editing, and visual direction.

Keyword research is fair game. Study popular channels in your genre and look at their titles, descriptions, hashtags, video tags, playlist names, and thumbnail wording.

Using tag extractors on successful videos is fine. Do not copy someone’s brand, songs, lyrics, or exact packaging, but copying common searchable keywords is normal SEO.

Example title:

Midnight Chill - 1 Hour Relaxing House Music

Example tags:

ai music, original ai music, chill music, relaxing music, lofi music, study music, background music

7. Post consistently, but do not spam

Consistency helps YouTube understand your channel, but only if the content is good enough.

A good starting schedule is one long mix per 1-2 weeks.

Avoid dumping dozens of similar videos at once. Regular uploads are better than mass uploads.

8. Watch your analytics

Do not judge only by views. A video with fewer views but good retention may be more useful than a video with many low-quality clicks.

Watch CTR for thumbnail and title strength, average view duration for music retention, the retention graph for where people leave, returning viewers for real audience interest, comments for emotional connection, and traffic source for how YouTube is finding viewers.

Quick diagnosis:

Low CTR usually means a title or thumbnail problem.
Good CTR but bad retention usually means a music, intro, or visual mismatch.
Good retention but low impressions may mean a niche or channel momentum issue.

Use the data to decide what to repeat, improve, or stop doing.

9. Be honest about AI

Be transparent, but do not make “AI” the whole brand. Most listeners care more about the music experience than the tool.

Better branding:

Relaxing original music playlists

Weaker branding:

AI-generated AI music made by AI

Simple disclosure:

This channel features original music created with AI-assisted production, human curation, editing, and visual direction.

Let the niche and mood lead. Let the AI disclosure be clear but secondary.

10. Avoid risky shortcuts

Original songs are safest when starting. Covers, artist imitation, and copied melodies can create copyright or monetization problems.

Avoid covers unless you understand copyright and monetization rules, famous melodies or lyrics, artist names in titles, “sounds exactly like [artist],” AI vocals imitating real singers, bought views/subscribers/comments/watch time, and repetitive near-identical uploads.

Don't do sub for sub. Don't pay for any promotion service. Don't bot.

Shortcuts can hurt the channel more than they help. You get banned for this, you get banned for life, along with other channels you have that might not even use AI.

Best simple formula

Original niche music + strong thumbnail + 45–60 minute playlist + searchable title + consistent uploads + analytics-based improvement.

Example first upload:

Title:
Midnight Chill - 1 Hour Relaxing Original Music

Thumbnail text:
MIDNIGHT CHILL
1 HOUR RELAXING MUSIC

Description opener:

#LoFi #ChillMusic #RelaxingMusic

Original AI-assisted music playlist for relaxing, studying, working, driving, or late-night background listening. Created with human curation, editing, and visual direction.

Someone posted an online test where you listened to 10 songs and had to guess AI or not. It got me thinking, do I have any songs I think would pass?

Post a song if you really think it sounds real for 30 seconds. Before the link, put the time stamp of when we should start listening for the 30 seconds.

If the 30 seconds DOES sound like AI, let's hear some constructive criticism or very light ribbing of why it sounds like AI 😄

I recently came across this survey on how music producers actually view and use AI music tools. I think it pretty interesting so wanted to share.

It was commissioned by Sonarworks and conducted by Sound On Sound. Over 70% of respondents are working professionals with more than a decade of experience, so it feels fairly credible and representative.

Current usage: About one in five are already regular AI users, nearly half are in the experimental stage, and fewer than 20% have no interest in AI at all.

Biggest concerns: Over a third worry that using AI would compromise their creative identity. Almost as many have ethical concerns,  particularly regarding copyright and training data legalit. And more than a quarter feel current AI tools do not yet meet professional quality standards.

Where they're most open to AI: Close to three-fifths are on board with AI handling repetitive technical tasks like vocal tuning, drum editing, and file management. Acceptance drops significantly when it comes to AI making creative decisions.

Views on the future: Only 3.6% think AI is a passing fad, and nearly a third believe it's already revolutionizing the industry. 58% see AI eventually settling into a supportive role, with humans keeping creative control.

The part I found most interesting: Jazz, blues, and classical music are seen as the hardest genres for AI to replicate, while EDM and mainstream pop are considered the easiest for AI to generate and replicate.

https://www.soundonsound.com/music-business/ai-music-tech-2026

[supprimé]

'Raise Your Blades or Watery Death'

Sean Williamson

Symphonic metal made with SUNO AI.