Audio to MIDI Conversion — How to Turn Any Song into MIDI (2026)

What Is Audio to MIDI Conversion?

Audio to MIDI conversion is the process of analyzing a recorded audio signal and transcribing the musical content into MIDI data — a digital representation of notes, timing, velocity, and duration. Instead of working with a fixed waveform that can only be stretched, pitched, or filtered, MIDI gives you access to the actual musical information underneath: which notes were played, when they started and stopped, and how hard they were hit. This transforms a static recording into something you can edit note-by-note, rearrange, transpose, and assign to entirely different instruments.

For producers and musicians, audio to MIDI conversion unlocks workflows that would otherwise require transcribing by ear or having access to the original session files. You can extract the bass line from a funk record and reassign it to a synthesizer. You can pull the vocal melody from a pop song and study its intervals, phrasing, and rhythmic placement. You can grab the drum pattern from a breakbeat and trigger your own samples with it. Transpose a melody to a different key in seconds. Layer a second instrument on top of an existing part with perfect timing. Study the music theory behind any song by seeing the actual notes laid out in a piano roll. The creative possibilities are enormous.

The fundamental challenge with audio to MIDI conversion is that real-world recordings contain multiple instruments playing simultaneously. When you try to transcribe a full mix directly, the algorithm has to untangle overlapping frequencies from bass, drums, vocals, guitars, keyboards, and everything else happening at once. The result is usually a mess of conflicting note data that's unusable in practice. A bass note and a kick drum occupy similar frequency ranges. A vocal melody and a lead guitar overlap in pitch. Harmonic overtones from one instrument bleed into the detection range of another. This is why the most effective approach to audio-to-MIDI conversion is to separate first, then transcribe — isolate each instrument into its own clean stem, and then run dedicated transcription on each stem independently.

How Qie Converts Audio to MIDI

Qie Stem Slicer takes a two-stage approach to MIDI conversion: first it separates your song into individual instrument stems using advanced AI neural networks, and then it runs specialized transcription algorithms on each separated stem. This separation-first approach produces dramatically better MIDI than any tool that tries to transcribe a full mix directly, because each transcription engine only has to deal with one instrument at a time.

Melodic Stems: Bass, Melody, and Vocals

For melodic instruments — bass, melody, and vocals — Qie uses Spotify's basic-pitch neural network for MIDI transcription. Basic-pitch is a lightweight convolutional neural network specifically designed for monophonic and polyphonic pitch detection. It analyzes the audio spectrogram and outputs note events with precise onset times, durations, pitches, and velocity estimates.

What makes Qie's implementation different from running basic-pitch on a raw mix is the per-stem tuned frequency profiles. Each stem type gets its own optimized configuration that constrains the detection to the frequency range where that instrument actually lives. The bass profile focuses on the 30 Hz to 500 Hz range, filtering out any residual high-frequency content that might cause false note detections. The melody profile covers 80 Hz to 8,000 Hz, capturing the full range of guitars, keyboards, strings, and other harmonic instruments. The vocal profile is tuned to 80 Hz to 4,000 Hz, matching the typical range of human singing voices while excluding sibilance, breath noise, and high-frequency artifacts that would otherwise generate spurious MIDI notes.

These per-stem profiles mean each MIDI file contains only the notes that actually belong to that instrument, with minimal false positives. The bass MIDI captures the low-end foundation without picking up kick drum hits. The melody MIDI tracks the harmonic content without confusing it with vocal vibrato. The vocal MIDI follows the singer's pitch contour without drifting into guitar harmonics. This level of instrument-specific tuning is only possible because the stems have already been cleanly separated before transcription begins.

Drum Stems: Onset Detection and General MIDI Mapping

Drums require a fundamentally different approach than melodic instruments. Drum sounds are percussive and largely unpitched — you can't meaningfully assign a “note” to a kick drum the way you can to a bass guitar. Instead of pitch detection, Qie uses librosa-based onset detection to identify the precise timing of each drum hit within each separated drum component stem (kick, snare, hi-hat, cymbal, ride, tom).

Each detected onset is mapped to the appropriate General MIDI drum note number, following the standard that virtually every DAW and drum plugin recognizes: kick drum = note 36, snare = note 38, closed hi-hat = note 42, open hi-hat = note 46, crash cymbal = note 49, ride cymbal = note 51, and toms across notes 48, 45, and 43 (high to low). When you load these MIDI files into your DAW, the notes automatically trigger the correct drum sounds without any manual remapping.

For the hi-hat stem, Qie applies additional spectral analysis to classify each hit as either open or closed. Open hi-hat hits have a longer spectral decay with more energy in the mid and high frequencies, while closed hits are shorter and tighter. This classification determines whether each MIDI note is assigned to note 42 (closed) or note 46 (open), preserving the musical articulation of the original performance. This distinction matters enormously for groove — the interplay between open and closed hi-hat hits defines the feel of a drum pattern far more than the note placement alone.

The key insight behind Qie's approach is that separating the audio first, then transcribing each stem independently, produces vastly more accurate MIDI than attempting to transcribe a complete mix or even a combined drum stem. When onset detection runs on an isolated kick drum stem, it only detects kick hits — there are no snare transients or hi-hat clicks to confuse the algorithm. When basic-pitch runs on a clean bass stem, it only sees bass frequencies — there are no competing melodic lines to generate false notes. This two-stage pipeline is what makes the difference between MIDI output that requires hours of manual cleanup and MIDI output you can actually use immediately.

Step-by-Step: Convert a Song to MIDI

Converting a song to MIDI with Qie takes just a few minutes. Here's the complete workflow from audio file to editable MIDI data.

Step 1: Import Your Audio

Open Qie Stem Slicer and drag your song directly into the application window. Qie accepts all common audio formats: WAV, MP3, FLAC, AIFF, OGG, M4A, and more. There's no need to convert or pre-process your files. You can also use the file browser to navigate to your audio. Qie handles full-length songs — no need to trim or split beforehand.

Step 2: Set BPM and Process

Qie auto-detects the BPM of your song. Confirm or adjust the tempo if needed, then click “Process Audio.” The AI pipeline runs in sequence: first the advanced AI neural networks separate your song into individual instrument stems (bass, drums, vocals, melody, plus 6 individual drum components). Then the MIDI transcription engines run on each separated stem — basic-pitch for melodic instruments, onset detection for drums. Processing typically takes 2 to 5 minutes depending on song length and your hardware.

Step 3: Find Your MIDI Files

Once processing completes, your output folder contains MIDI files for every stem: bass, melody, vocals, full drums, kick, snare, hi-hat, cymbal, ride, and tom. Each MIDI file corresponds to its audio stem counterpart, so you can audition the separated audio and its MIDI transcription side by side. The MIDI files are also chopped into 8-bar segments that match the audio loops, making it easy to grab specific sections.

In addition to individual MIDI files, Qie generates an Ableton Live MIDI project (.als file) with all MIDI tracks pre-loaded and organized. Open the project in Ableton and you have every instrument's MIDI on its own track, color-coded and arranged on the timeline. This is the fastest way to start editing, reassigning instruments, or building a remix from the transcribed MIDI data. You can also import the individual MIDI files into any other DAW — Logic Pro, FL Studio, Pro Tools, Cubase, Reaper, or any application that reads standard MIDI files.

Audio to MIDI Tools Compared

Several tools offer some form of audio to MIDI conversion, but they differ significantly in approach, accuracy, and output format. Here's how the major options compare. For a full feature comparison across all separation and transcription capabilities, see the complete stem separator comparison.

The core differentiator is the separation-first approach. Tools that attempt to convert a full mix to MIDI in one step inevitably produce noisy, inaccurate results because multiple instruments occupy overlapping frequency ranges. Ableton's built-in audio-to-MIDI works well on isolated recordings (a single vocal take, a solo bass recording) but struggles with full mixes. Basic-pitch is an excellent transcription engine — it's actually what Qie uses under the hood — but running it on a raw mix without separation first gives poor results. Qie combines best-in-class separation with best-in-class transcription, applying instrument-specific tuning at every stage.

Tips for Better MIDI Conversion

While Qie's separation-first approach handles most songs well out of the box, there are several things you can do to get the best possible MIDI output from your audio files.

Clean separation improves MIDI accuracy. The quality of the MIDI transcription depends directly on the quality of the stem separation. Songs with clear, well-defined instrument parts — where each instrument occupies its own frequency space and doesn't overlap too heavily with others — produce the cleanest separations, which in turn produce the most accurate MIDI. Well-produced studio recordings generally convert better than lo-fi recordings, live performances with heavy room ambience, or tracks with extreme compression that smears the transients together.

Simpler arrangements convert better. A song with a bass, drums, one vocal, and one melodic instrument will produce better MIDI than a dense orchestral arrangement with dozens of layered parts. This isn't a limitation of the MIDI transcription itself — it's a consequence of separation quality. The fewer overlapping instruments in the original mix, the cleaner the separation, and the cleaner the separation, the better the MIDI. If you're specifically targeting MIDI extraction, songs in the pop, rock, hip-hop, R&B, and electronic genres tend to produce the best results.

Use MIDI as a starting point, then edit. Even the best AI transcription is not perfect. Think of the generated MIDI as a very fast first draft that gets you 80-90% of the way there. You'll likely want to open the MIDI in your DAW's piano roll editor and clean up a few things: remove occasional false notes, adjust note lengths where the algorithm held a note too long or cut it too short, and fine-tune velocities. This is still dramatically faster than transcribing entirely by ear, which can take hours for a single song. The MIDI gives you the structure, timing, and pitch information — you just need to polish the details.

Higher quality source audio helps. Start with the highest quality audio file you have. Lossless formats like WAV, FLAC, or AIFF preserve more detail in the transients and harmonics that the separation and transcription algorithms rely on. Heavily compressed MP3 files (especially at low bitrates like 128 kbps) can introduce artifacts that affect both separation quality and MIDI accuracy. If you have a choice between a 128 kbps MP3 and a WAV or high-bitrate FLAC, always use the higher quality source.

Frequently Asked Questions