Audio to MIDI Transcription in 2025 – How Good Is It (Really)?

Introduction: The Discernment Gap. You still need a human.

While the rapid evolution of AI-driven audio-to-MIDI tools has undeniably changed the landscape of music production, a significant gap remains between raw data capture and musical intelligence. Current software has become remarkably adept at transcribing solo performances, yet it consistently falters when faced with the complex textures of an ensemble. The technical reality is that while an algorithm can analyze frequencies, it lacks musical discernment—the ability to distinguish a primary melodyA sequence of single pitches perceived as a unit, usually the main theme or tune in a piece of music. from a cluster of overlapping harmonicsOvertones that are present in a sound, which contribute to its timbre. or to identify the subtle timbral “signature” of a specific instrument in a dense mix1. Collection of individual tracks or parts 2. The process of adjusting relative sound levels, processing and placement within a sonic realm 3. The result of sonic recording and processing. For the professional, this often leads to a paradoxical workflow where fixing “hallucinated” MIDIA protocol for communicating musical information, such as notes and control signals, between electronic musical instruments and computers. notes takes longer than the task itself. In many cases, aural transcriptionThe process of notating a piece of music as it is performed, either by ear or from a recording. remains the faster, more efficient path, simply because a human ear can make instantaneous context-based decisions that an AI, bound by statistical patterns rather than musical intent, cannot yet replicate.

1. The “Source Separation” vs. “Transcription” Dilemma

In the current landscape of Music Information Retrieval (MIRMIR is an acronym for Music Information Retrieval, pertaining to a class of digital tools that analyze and attempt to transcribe musical content.), tools attempting to transcribe a multi-instrument mix generally follow one of two architectural paths. Understanding these paths is crucial for any professional transcriber, as each introduces specific “artifacts” that require human intervention.

Path A: Source Separation First (The “Unmixing” Approach)

This method uses AI to “unmix” a finished song into individual stemsIndividual tracks extracted or exported from a mix as individual sound files (e.g., separating the bass, drums, and vocals) before running a transcription engine on each result.

• The Workflow: Audio → AI Stem Splitter → Individual MIDI Files.
• The Human Requirement: This process is rarely “clean.” Separation often leaves behind “spectral ghosting”—bits of a snare drum might bleed into the piano stem. A transcription tool will often mistake these artifacts for actual notes (hallucinations), requiring a human to manually prune hundreds of “ghostRefers to original composition done as work-for-hire, without credit (we do this) notes” from the MIDI roll.

Path B: The “Direct” Sequence Approach (MT3)

While the “Source Separation” method attempts to pull the audio apart, a newer and more ambitious approach treats transcription like a translation problem. The gold standard for this is Google Magenta’s MT3An audio-to-midi project in development at Google attempting to isolate instruments in an ensemble and transcribe them to MIDI. Obstacles include overtones, resultant tones, conflicts in sonority, and the inability to detect a through line. (Multi-Task Multitrack Music Transcription).

Instead of looking at a spectrogram as a visual image of notes, MT3 uses a Transformer architecture—the same fundamentalLowest, most predominant perceived pitch of a musical tone technology behind Large Language Models like ChatGPT. It “reads” the audio and outputs a stream of musical tokens (e.g., [PIANO][Note_On][C4]).

• The Breakthrough: MT3 is one of the first models capable of transcribing arbitrary ensembles (jazz trios, string quartets, etc.) at once. It doesn’t need to be told what instruments are playing; it attempts to classify the timbreThe unique tonal quality of a sound, often described in terms of its texture, color, or tone quality. on the fly.
• The Human Requirement: Despite its sophistication, MT3 suffers from “Instrument Leakage.” Because it processes audio in short time-segments, it lacks long-term musical memory. It might correctly identify a melody on a violin track but suddenly “decide” mid-phrase that the notes now belong to a flute. A human transcriber understands the continuity of a performance; the AI only sees a statistical probability, leading to fragmented MIDI files that require extensive manual “re-stitching.”

Resource: Try it Yourself

If you want to see the “Discernment Gap” in action, you can run your own audio through the official research models. These are hosted on Google Colab, allowing you to use Google’s cloud GPUs for free:

• Official MT3 Colab Notebook: This is the original Magenta tool. You can upload a WAV or MP3 and watch it attempt to generate a multi-track MIDI file.
• YourMT3+ Demo (HuggingFace): A 2024/2025 community evolution of the MT3 architecture that includes better support for singing voice transcription and improved “mixture of experts” for identifying instruments.

I tried these and couldn’t get them to run. They’re very much a work-in-progress.

The hidden ‘tax’ of modern AI transcription is the environment setup. While an aural transcriber’s ‘boot time’ is measured in the seconds it takes to put on headphones, a state-of-the-art AI like MT3 requires a multi-step provisioning process. In the time it takes to resolve a ‘ModuleNotFoundError’ or wait for a 1GB model checkpoint to download, I kept working aurally and did another 30 bars waiting for it to load.

Why Aural Transcription is Often Faster

This leads to the core argument for the professional transcriber: Efficiency.

When using tools like MT3 on a complex mix, the “cleanup” phase is not merely moving a few notes. It involves:

1. De-ghosting: Removing notes triggered by drum transients or room reverba type of audio effect that simulates the natural reflection of sound in a room..
2. Re-assignment: Dragging notes from the “Flute” track back to the “Violin” track where they belong.
3. VelocityRefers to the amount of energy applied by a performer in generating a tone, and is communicated in MIDI as an attribute of a note. Sampled instruments usually contain sounds produced at varying velocities, which are mapped to MIDI velocity ranges in the sample player. A MIDI note with a higher velocity will sound as if it's performed with more energy than one with less velocity. Velocity is expressed as a number in a range of 1-127 Correction: AI often struggles with the “feel” or dynamicsThe relative loudness or softness of an element of piece of music, indicated by symbols, or controlled by MIDI values of a performance, resulting in robotic 127velocity MIDI blocks.

A professional with a trained ear performs these three tasks simultaneously during the first pass of an aural transcription. By the time the AI user has finished cleaning up the “hallucinations” of a transformer model, the aural transcriber has often already moved on to the next chart.

2. The Timbre Bottleneck: Why Machines Struggle

The primary reason current tools fail at multi-instrument transcription is the complexity of the Harmonic SeriesArray of notes contained within a pitch with a mathematical (rationic) relationship to the "fundamental," which is the container note for all the rationic subcomponents. Also see "Overtone Series". Every instrument is defined by its unique recipe of overtones.

In a complex arrangementrefers to the structure and order of musical elements in a composition, such as melody, harmony, rhythm, and instrumentation., these overtones overlap. If a trumpet’s third harmonic lands exactly on a flute’s fundamental frequencya measurement of the number of oscillations per second of a sound wave, which determines its pitch., the AI faces a “collision.” While a human listener uses musical context and psychoacoustics to distinguish the two, an AI often sees a single blurred data point.

Key Technical Hurdles:

• Harmonic Collision: Deciding which instrument “owns” a shared frequency.
• Occlusion: A loud, harmonically rich instrument (like a distorted guitar) can physically mask the subtle spectral signatures of a cleaner instrument (like a piano).
• Transient Blurring: The “click” of a guitar pick or the “thump” of a piano hammer is vital for identification, but these often get lost in a dense mix.

Conclusion: The Future of the “Human Filter”

We are trackingthe process of recording individual instrument or vocal tracks in a music production. the evolution of AI transcription with genuine fascination. The results from the 2025 AMT Challenge show that models like YourMT3+ and MusicFM are making significant strides in handling richer contexts and longer audio windows. We aren’t luddites; we see the potential for these tools to eventually handle the “heavy lifting” of data entry.

However, for the high-stakes world of professional arrangement and session charting, the “Human Filter” remains our most valuable asset. The implications for our business are clear: while AI is learning to identify notes, it has yet to learn how to identify musical intent. For now, our ears remain the gold standard, ensuring that every chart is free of “spectral ghosts” and track leakage. We’re watching the machines work on it—but until they can tell a deliberate blue note from a digital artifact, we’ll be sticking to the method that actually saves our clients time – and money: aural transcription.

Summary

Article Name

Audio to MIDI Transcription in 2025 - How Good Is It (Really)?

Description

AI audio-to-MIDI tools like MT3 promise a lot, but need a lot of cleanup. Why aural transcription is still faster for ensembles in late 2025.

Author

Jon Burr

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Arranger for Hire

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