Converting audio to MIDI is the process of taking a recording and turning it into a list of notes: pitch, start time, duration, velocity. Once you have MIDI, you can edit individual notes, change instruments, generate sheet music, or feed the data into any DAW. It's a foundational conversion, and the tools that handle it well in 2026 are noticeably better than what was available even two years ago.
What follows is a working musician's walkthrough of how the conversion actually works, what the tradeoffs are, and which tools fit which situations.
What "Audio to MIDI" Actually Means
A piece of audio is a continuous waveform, a recording of pressure changes over time. MIDI is the opposite: discrete events with exact pitches and timestamps. Converting between them means making decisions the audio doesn't answer on its own. Where exactly does this note start? Is that fluctuation a separate note or vibrato on a held one? Is the player on a B♭ or just a slightly flat B?
Modern audio-to-MIDI tools use machine learning models trained on millions of hours of audio paired with ground-truth MIDI. They've seen enough examples to make those judgment calls in a way that matches how a human transcriber would. They're not perfect (no model is), but they're close enough that the workflow has shifted from "transcribe by ear and use the tool to check" to "run the tool first and clean up after."
Monophonic vs Polyphonic Conversion
The distinction matters because the underlying problems are different. Monophonic conversion handles single-note-at-a-time sources: vocals, lead guitar, saxophone, melody whistled into a phone. The tool has to figure out pitch and timing, but it never has to separate overlapping notes.
Polyphonic conversion handles sources where multiple notes happen at once: piano, guitar chords, full mixes. This is much harder. The model has to identify each pitch in a chord, decide which notes belong to which instrument if the source has more than one, and handle the partial overlaps when one note rings into the next. Polyphonic conversion has gotten dramatically better in the last few years, but it still benefits from clean source material.
The Tools That Work in 2026
Songscription
Songscription takes a per-instrument approach focused on a smaller set of instruments where the model quality is highest: piano is the strongest, with additional models for acoustic guitar, drums, violin, flute, saxophone, trumpet, bass, and a few others. Handles polyphonic input (full piano recordings, guitar chords, mixed audio) and exports both MIDI and MusicXML. The in-platform notation and piano roll editor lets you scrub through the result against the original audio and fix anything the model gets wrong without leaving the platform, which is the part that matters most when you're cleaning up output. The workflow also extends past raw MIDI into arrangement and difficulty leveling if you need notation, not just a MIDI file.
Klangio
Per-instrument transcribers across a slightly wider range of instruments than Songscription, plus an API and DAW plugins, which is the biggest functional difference. If you're building audio-to-MIDI into your own software, or you want it to live inside your DAW rather than a separate web app, Klangio is set up for that. On the instruments both products cover, Songscription tends to produce cleaner results, particularly on piano.
Logic Pro's Flex Pitch (and similar DAW features)
Most modern DAWs include some form of audio-to-MIDI conversion. They tend to be best at monophonic sources like humming a melody into a microphone or converting a vocal line. For anything polyphonic, the dedicated tools usually outperform DAW features by a noticeable margin.
Basic Pitch (Spotify's open-source model)
Free and open-source. Basic Pitch is a polyphonic model that runs locally and produces reasonable MIDI from clean recordings. It's not as accurate as the leading commercial tools, but it's free and it runs offline, which matters for some workflows. If you need a starting point for a quick conversion and don't want to upload audio anywhere, it's worth knowing about.
How to Get the Best Results
Start with the cleanest source you have
A studio recording of a piano transcribes far better than a phone recording of the same piano in a noisy room. Background noise, room reflections, and compression artifacts all confuse the model. If you have access to the original stems or a high-quality recording, use them.
Split stems before transcribing full mixes
If you're converting a full song to MIDI, separating the instruments first almost always helps. Run the mix through a stem splitter (Moises, LALAL.AI, or similar), then transcribe each instrument individually. The piano-only stem will produce a much better MIDI file than feeding the full mix to a piano transcriber.
Pick the right tool for the source
A vocal melody and a piano performance need different models. Some tools have separate transcribers for different instruments; some don't. Using a piano-specific model on a guitar recording will give you worse results than a general-purpose tool, because the model is making instrument-specific assumptions that don't hold.
Plan to clean up the output
Even the best tools produce MIDI that benefits from a pass of human editing. Common cleanup tasks: removing spurious short notes (artifacts of a noisy source), fixing octave errors on bass notes, adjusting note durations where the tool ended a note too early or held it too long. Budget 10–20 minutes of editing per song and you'll have something usable.
From MIDI to Sheet Music
Once you have MIDI, generating sheet music is a separate problem with its own decisions: quantization, hand splits, key and time signature, articulations. Some tools handle audio → MIDI → notation as a single pipeline; others stop at MIDI. If sheet music is the goal, see our guide on converting MIDI to sheet music for what happens after the MIDI export.
Final Thoughts
Audio-to-MIDI conversion is now reliable enough to build into a real workflow. The remaining limitations are mostly about source quality. Give a good model a clean recording and you'll get usable MIDI in seconds. Give it a phone recording from across the room and the result will need work no matter how good the tool is.
The tools have caught up to the point where the bottleneck is no longer the conversion itself. It's deciding what you want to do with the result. A producer wants MIDI they can drop into a DAW. A composer wants notation they can edit. A music teacher wants something they can hand to a student. The same audio file becomes three different artifacts, and the value of these tools is that the conversion step is no longer the slow part of any of those workflows.