Wav2Midi: Convert Audio to MIDI with AI

Wav2Midi: Convert Audio to MIDI with AIAudio-to-MIDI conversion has long been a sought-after tool for musicians, producers, and audio engineers. Translating recorded sound into editable MIDI makes it possible to change instrumentation, correct performances, analyze musical structure, and repurpose ideas across projects. Wav2Midi — an umbrella term for systems that convert WAV (or other audio) files into MIDI representations — leverages modern AI to produce more accurate, musically useful results than older, rule-based approaches. This article explains how Wav2Midi works, surveys current techniques, examines practical workflows, discusses limitations, and offers tips to improve results.

What is Wav2Midi?

Wav2Midi refers to tools or models that take an audio file (commonly WAV) as input and output a MIDI file containing note events, timing, velocities, and sometimes additional metadata like instrument assignments, key, or tempo. Unlike simple pitch-detection utilities, advanced Wav2Midi systems aim to transcribe polyphonic audio (multiple notes at once), preserve rhythmic feel, and capture expressive details such as velocity and articulations.

Why convert audio to MIDI?

Converting audio to MIDI unlocks many creative and practical possibilities:

• Edit pitch, timing, and expression of recorded performances without re-recording.
• Replace recorded instruments with virtual instruments or synths.
• Extract melodies, chord progressions, and harmonic analyses for study or remixing.
• Create notation or tablature from audio.
• Automate MIDI-driven effects and sequencing from live performances.

How modern AI-based Wav2Midi works

AI-based Wav2Midi systems use machine learning — primarily deep learning — to map audio waveforms or time-frequency representations to symbolic MIDI events. Key components and approaches include:

• Input representation:

  • Short-time Fourier transform (STFT) or mel-spectrograms convert raw audio into time-frequency images that models can analyze.
  • Raw waveform models process audio samples directly with convolutional or transformer architectures.

• Neural architectures:

  • Convolutional Neural Networks (CNNs) extract local spectral features useful for onset and pitch detection.
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) layers model temporal dependencies.
  • Transformers capture long-range relationships and can model complex polyphony and timing.
  • Hybrid models combine CNN frontends with transformer or RNN backends.

• Output representation:

  • Frame-wise pitch probabilities assign pitches per time frame and then use heuristics to group frames into notes.
  • Onset/offset detectors focus on identifying precise note starts and ends, improving timing accuracy.
  • Event-based tokenization represents musical events (note_on, note_off, time_shift, velocity) similar to language modeling.

• Training:

  • Supervised learning on paired audio–MIDI datasets.
  • Data augmentation (pitch-shifting, time-stretching, mixing) to improve model robustness.

• Post-processing:

  • Voice separation and note grouping to form clean MIDI tracks.
  • Tempo and beat tracking to align MIDI to musical time.
  • Quantization and smoothing to remove spurious artefacts.

Popular approaches and projects

Several research projects and open-source tools have advanced audio-to-MIDI transcription:

• Piano-focused systems: Models like Onsets-and-Frames and its successors specialize in piano transcription and achieve high accuracy by modeling onsets, frames, and velocity.
• Multi-instrument and general transcription: More recent transformer-based models aim to transcribe polyphonic multi-instrument audio into multi-track MIDI, though challenges remain for dense mixes.
• Commercial tools: DAWs and plugins offer audio-to-MIDI features — results vary depending on source material complexity.

Practical workflow: From WAV to usable MIDI

1. Preprocess audio:

   • Use a lossless WAV file at a standard sample rate (44.1–48 kHz).
   • Clean up noise and apply mild EQ to emphasize fundamentals if possible.
   • If the recording contains many instruments, consider isolating the target source (vocals, guitar, piano) with source separation tools.

2. Choose a Wav2Midi tool or model:

   • For piano, use a piano-specialized model for best results.
   • For monophonic instruments (voice, flute, single guitar lines), pitch-tracking tools work well.
   • For polyphonic mixes, try advanced AI models that support multi-instrument transcription.

3. Run transcription:

   • Provide clear audio and, if possible, specify instrument type or expected tempo.
   • If the tool allows, enable onset/offset detection and velocity estimation.

4. Post-process MIDI:

   • Correct obvious errors: remove spurious notes, merge duplicates, adjust velocities.
   • Quantize timing as needed, preserving swing or human feel when desired.
   • Assign instruments and map channels in your DAW or MIDI editor.
   • Use MIDI effects, humanization, or further editing to refine the performance.

Strengths and limitations

• Strengths:

  • AI improves transcription for complex polyphony and expressive dynamics.
  • Fast iteration: change sounds without re-recording.
  • Great for extracting ideas, creating arrangements, and analysis.

• Limitations:

  • Dense mixes and heavy effects (distortion, reverb) reduce accuracy.
  • Separating overlapping harmonics or similar timbres is still challenging.
  • Percussive or noisy instruments may transcribe poorly into pitched MIDI.
  • Models trained on specific instruments/general datasets may bias results.

Tips to improve transcription quality

• Record dry, with minimal effects and good instrument separation.
• Use isolated stems or apply source separation when working from full mixes.
• Prefer higher-quality models trained on similar instruments.
• Manually correct and humanize output rather than relying solely on automatic results.
• Experiment with model settings: onset sensitivity, minimum note length, and velocity thresholds.

Example use cases

• A composer records a humming melody, converts it to MIDI, then assigns a synth patch to build full arrangement.
• A producer extracts piano chords from a demo take to replace with sampled grand piano while preserving feel.
• An educator transcribes student performances for feedback and notation.
• A remixer extracts vocal pitch contours to MIDI for harmonization and pitch-shifted effects.

Future directions

Expect ongoing improvements as models and datasets grow:

• Better multi-instrument transcription with separation-aware models.
• End-to-end systems that directly output multi-track MIDI with instrument labels.
• Real-time Wav2Midi for live performance, enabling expressive controllers driven by audio.
• Integration with music notation and DAW ecosystems for seamless production workflows.

Conclusion

Wav2Midi, powered by modern AI, turns recorded audio into editable MIDI with increasing accuracy and musicality. While limitations remain—especially in complex mixes and non-pitched sounds—the technique empowers creators to repurpose performances, streamline production, and explore new workflows. By choosing the right tools, preparing good audio, and applying thoughtful post-processing, you can get highly useful MIDI transcriptions from your WAV files.