How to Create Backing Tracks for Practice with AI Stem Splitter

Most musicians' practice routine has one obvious gap: there's no band.

You can metronome through a song for an hour, but you won't learn how to land the chorus until you're playing against actual drums, actual bass, actual vocals. The classic answer was buying backing tracks one at a time on iTunes — a few hundred songs, $1.99 each, mostly bad mixes of songs you don't want to play.

AI source separation killed that market. You can now take any song you own (or any YouTube link) and remove your instrument in a few minutes. The result is a backing track that fits the original record exactly, because it is the original record minus you.

This post walks through the practical workflow for the four common cases — voice, guitar, bass, drums — plus the songs where the trick doesn't work, and what to do when you need to slow them down.

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What you'll end up with

A single audio file per song that contains the full original recording minus your instrument. Drop it into Spotify on your phone, Anytune, a portable looper, or any DAW. Play along.

For singers, that's the karaoke instrumental. For guitarists, the full band without guitar. For drummers, the song with a hole where your kit goes. Same idea, different stems removed.

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Pick the right model first

This is the one decision most people get wrong, and it costs you a full re-render.

Your instrument	Use this model	Why
Voice (singing)	4-stem (default)	Vocals separate cleanest in the 4-stem model
Bass	4-stem (default)	Bass has its own dedicated stem
Drums	4-stem (default)	Drums have their own dedicated stem
Guitar	6-stem	Without 6-stem, guitar gets dumped into "other" with synths and strings
Piano	6-stem	Same reason — piano needs its own dedicated stem
Sax, violin, brass	4-stem (and accept it)	No dedicated stem exists; they live in "other"

The 6-stem model is the one mistake we see most often. Guitarists default to 4-stem out of habit, then wonder why their "instrumental" backing track still has guitar bleeding through. It's not a model bug — there's no dedicated guitar stem in the 4-stem model. Pick 6-stem if you play guitar or piano. Otherwise pick 4-stem; it's faster and slightly cleaner per stem.

The cost is the same either way, so don't optimize for it. (We wrote up the per-call cost math here.)

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Workflow: singing practice

This is the simplest case because "everything except vocals" is one click.

1. Pick a song. Anything with clean production. Avoid live recordings (everything bleeds) and songs where the lead vocal is doubled, heavily auto-tuned, or buried under reverb.
2. Skip stem splitter, use the karaoke maker instead. It's a one-click "give me the instrumental" version of this exact workflow.
3. Wait about 60 seconds. A 3-minute song processes in roughly that time end-to-end.
4. Download the instrumental file. That's drums + bass + other already mixed down. Drop into your phone. Done.

The one trick: if the song has prominent backing vocals you also want to remove (Beatles-style stacked harmonies), the karaoke maker leaves those in. There's no model on the public internet that cleanly separates lead vocals from backing vocals — they share too much frequency content. Pick a different recording, or accept the backing vocals in your instrumental.

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Workflow: guitar practice

This is where the 6-stem decision matters.

1. Pick the song. Songs with one clearly recorded guitar work best — clean tones, well-separated channels. Songs with five layered guitar tracks (most metal, a lot of modern pop) are a hard case for any model.
2. Open AI Stem Splitter and choose 6-stem. Upload the file or paste a YouTube URL.
3. Wait 2–3 minutes for processing.
4. Download all stems except guitar. You'll get six files: vocals, drums, bass, guitar, piano, other. Keep five, skip guitar.
5. Mix them back into one file. Drag the five stems into Audacity (free) or any DAW. Set all tracks to 0 dB. Export as MP3.

The result is the full band minus guitar. Loop the solo section in any audio player that supports A-B repeat and practice the lick fifty times.

The timing trap: it's tempting to also mute drums for a "cleaner" practice mix. Don't. Most musicians lose timing without the drums as reference, and the whole point of playing along with the record is to learn how the part sits against the groove.

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Workflow: bass practice

Almost identical to guitar, but use 4-stem.

1. Upload the song to AI Stem Splitter, pick 4-stem.
2. Wait roughly 60 seconds.
3. Download vocals + drums + other. Skip the bass stem.
4. Mix them back together in Audacity. Export.

Bass-specific gotcha: songs with synth bass or heavy sub-bass often get split awkwardly between the "bass" stem and "other". If your bassline disappears from the bass file and shows up faintly in "other", the original mix routed the bass through a synth or used heavy sidechaining. There's no fix at the model level — pick a different song, or layer the two stems back together and accept that the "backing" track will have ghost bass in it.

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Workflow: drum practice

Same flow, different stem to drop.

1. Upload to AI Stem Splitter, pick 4-stem.
2. Download vocals + bass + other. Skip the drums stem.
3. Mix back to one file.

Drum-specific gotcha: the "vocals" stem will have faint cymbal hash bleeding through (cymbals share a lot of upper-frequency content with sibilant vocals), and the "other" stem will sometimes have ghost-snare artifacts. For practice, this doesn't matter — you'll be playing loud enough that nobody hears the bleed. For recording your kit over the backing track, high-pass everything except the drum slot at ~80 Hz and the bleed disappears.

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Songs that work, songs that don't

This is the half of the equation nobody talks about. A perfect model can't separate audio that wasn't recorded with separation in mind.

Works well:

• Classic rock (Beatles after '66, CCR, Tom Petty, Springsteen)
• Country, almost universally — vocal is always front and center
• Acoustic singer-songwriter
• Modern pop with clean production (most things post-2010)
• Jazz standards with small ensembles

Works poorly:

• Heavy shoegaze and lo-fi (intentional washy production)
• Heavy auto-tuned vocals doubled with effected harmonies
• Live recordings (everything bleeds into everything)
• Songs with heavy parallel/bus compression
• Pre-1965 mono mixes
• Heavy metal with layered guitar walls

The earbud test: if you can clearly hear and name each instrument when listening on cheap earbuds, the model can probably separate them. If the mix sounds like a wall of sound on cheap earbuds, the model will give you a wall of stems.

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Slowing down or changing key

A backing track at original tempo is rarely useful when you're still learning. Two ways to handle it.

Slow down after separation. Run the song through stem splitter normally, mix your backing track, then drop it into the slowed + reverb maker. Works fine for tempo drops up to about 15%. Beyond that you start hearing time-stretch artifacts on the cymbals.

Slow down before separation. Counterintuitively, this often produces better stem quality. The model processes the same audio at a lower sample density per second, which gives it more to work with on tricky transients. Try this for songs where the default separation comes out muddy.

For key changes, use the pitch changer on your final backing track. Avoid changing key before separation — the pitch-shift artifacts confuse the model and you end up with worse stems.

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Three pitfalls worth knowing

1. Don't normalize each stem before mixing. Stem separation already preserves relative volumes from the original mix. If you normalize each stem to 0 dB before combining, you'll get a backing track where the bass is suddenly the loudest thing — totally wrong against the original record. Import the raw stems, set all tracks to 0 dB gain, export.

2. Don't bother with stems for a one-off. Stem separation makes sense for songs you'll practice 50 times. For a song you'll play through twice, just play along with the original record at a volume that lets you hear yourself. The math of "5 minutes of processing + 30 seconds of mixing" only pays back across many practice sessions.

3. Don't trust the first separation if the source audio sounds bad. Bitrate matters. A 128 kbps YouTube rip will separate noticeably worse than a 320 kbps MP3 or a lossless file. If the result sounds off, check the source first — there's a real ceiling on quality you can extract from a low-bitrate source.

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What this looks like in practice

A typical workflow takes about three minutes of active time:

• 30 seconds to upload the song and pick the model
• 1–3 minutes of processing (you're not doing anything)
• 30 seconds to download and combine in Audacity

Total: under 5 minutes from "I want to practice this song" to "the backing track is on my phone."

If you only need vocals removed, the karaoke maker skips the manual mix step entirely. For everything else, one drag-and-drop into Audacity is the whole job.

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The takeaway: the model is the easy part. Picking the right model for your instrument and picking a song that was recorded with clean separation are the two decisions that determine whether you spend the next hour practicing or troubleshooting.

If you want to try it on a song without setting up a local toolchain, AI Stem Splitter is free for the first few minutes of audio.