AI can support this as an end-to-end production workflow: generate a musical idea, extract/analyze audio from an MP4, separate it into approximate stems, transcribe selected parts to MIDI, then rebuild and orchestrate it in a DAW. The key distinction is that “stems” separated from a finished stereo track are estimates—not the original studio multitrack session. github

What AI can do

Goal What AI does Typical output Good tools / approaches
Create a complete song Generates composition, arrangement, sound design, and sometimes vocals from a text prompt Stereo WAV/MP3; sometimes stems Suno, Udio, Stable Audio, AIVA
Compose orchestral music Produces cinematic/classical drafts from descriptions, tempo, mood, and structure Stereo render, MIDI-like editable material in some workflows AIVA, DAW MIDI composition, local models
Analyze music from MP4 Extracts audio; estimates tempo, key, chords, beats, loudness, sections, and possibly lyrics WAV + metadata, beat grid, chord/key timeline FFmpeg, librosa, Essentia, Sonic Visualiser
Split a song into parts Separates vocals, drums, bass, and “other”; some services offer more categories Individual WAV stems Demucs, Spleeter, Moises, LALAL.AI
Convert audio into notes Transcribes melodic or polyphonic audio into editable MIDI MIDI file, note/pitch events Basic Pitch, NeuralNote, Spotify Basic Pitch–style pipelines
Re-orchestrate Uses analysis/MIDI as a blueprint, then assigns musical lines to strings, brass, winds, percussion A DAW project with virtual instruments Logic Pro, Cubase, Reaper, Ableton, Dorico + orchestral libraries
Mix and master Detects balance/spectral issues and suggests or applies processing Assisted mix/master chain iZotope-style tools, DAW plugins, stem-aware analysis

For an orchestral result, AI is often most valuable as a sketching, transcription, and arrangement assistant. A final convincing score still benefits heavily from human decisions about voicing, articulations, register, dynamics, counterpoint, and realism.

MP4-to-music workflow

If you have an MP4 containing music, a practical technical pipeline is:

  1. Extract the audio 
    ffmpeg -i input.mp4 -vn -ac 2 -ar 44100 extracted.wav
    • -vn discards video.
    • Use WAV rather than repeatedly working with compressed audio.
  2. Inspect and analyze
    • Estimate BPM and beat positions.
    • Detect the global key and local harmonic changes.
    • Identify sections: intro, verse, chorus, bridge, outro.
    • Measure loudness, spectral balance, stereo width, and energy over time.
    • Transcribe lyrics separately if vocals are relevant.

    librosa is a common Python library for beat tracking, pitch analysis, MFCCs, spectrograms, and related signal-analysis tasks. github

  3. Separate approximate stems 
    python -m demucs -n htdemucs extracted.wav

    Demucs is an open-source source-separation tool commonly used to split a mix into vocals, drums, bass, and other accompaniment. workmusic

  4. Transcribe the parts that matter
    • Use vocal/piano/melodic stems to create rough MIDI.
    • Correct note timing, pitches, durations, and quantization manually.
    • Do not expect perfect transcription of dense orchestral or distorted guitar material.

    NeuralNote, for example, is an open-source tool aimed at turning recorded audio into editable MIDI, available as a DAW plugin and standalone tool. sourceforge

  5. Rebuild rather than merely process
    • Use the extracted stems and MIDI as references.
    • Re-perform or reprogram the musical parts.
    • Make a new orchestration and mix from your own virtual instruments.
    • This yields much more control than trying to “upgrade” a stereo track directly.

Building an orchestra

A robust orchestration workflow begins with a musical reduction: melody, bass, harmony, rhythm, and form. Then distribute those roles across an orchestra.

Suggested track layout

Family Example tracks Main role
Strings Violins I, Violins II, Violas, Cellos, Basses Harmonic bed, melody, rhythmic motion, emotional sustain
Woodwinds Flute, Oboe, Clarinet, Bassoon Color, countermelody, agile figures, exposed melodic lines
Brass Horns, Trumpets, Trombones, Tuba Power, climaxes, harmonic weight, fanfare motifs
Percussion Timpani, bass drum, cymbals, snare, mallets Accent, pulse, transitions, scale
Choir / texture SATB choir, pads, hybrid synths Atmosphere and cinematic scale

Example transformation

Suppose the MP4 contains a pop chorus with:

  • Lead vocal melody
  • Four-chord piano/guitar loop
  • Bass line
  • Kick/snare groove
  • Synth pad

A cinematic orchestral adaptation might become:

  • Violins I / solo flute: lead melody, alternating phrases.
  • Violins II and violas: divided chord voicings or repeated ostinato.
  • Cellos and basses: bass line, occasionally reinforced by bassoons/tuba.
  • Horns: broad chord support and call-and-response motif.
  • Trumpets/trombones: reserved for cadence points and final chorus lift.
  • Timpani and low percussion: harmonic roots and phrase-level accents.
  • Cymbal swells / taiko-like hits: transitions, but used sparingly.
  • Choir or soft synth layer: optional support for a hybrid film-score texture.

The AI-derived MIDI is only a starting point. Realism comes from expression lanes and articulations: legato versus détaché strings, breath-shaped wind lines, brass dynamics, velocity variation, timing offsets, and controlled reverb.

Tool choices

Fast, low-code route

Use this if you want results quickly without building a local pipeline:

  • Moises or LALAL.AI: upload a track/audio extracted from an MP4, create stems, slow down or change key, and practice/analyze parts. Moises has been expanding toward editable parts and DAW export, including MIDI-oriented workflows. ithub.global.ssl.fastly
  • Suno or Udio: generate an original musical concept from a detailed prompt, then export available stems for DAW work. Some tool directories report native stem workflows for Udio and stem/MIDI-oriented export options for Suno, though exact capabilities and plan limits change frequently. needaitool
  • AIVA: better aligned with classical, cinematic, and orchestral composition than generic full-song generators. ithub.global.ssl.fastly
  • Logic Pro / Cubase / Reaper: arrange the stems/MIDI, host instruments, and produce the final mix.

Local and programmable route

This aligns well with a Python/Linux/Docker workflow:

  • FFmpeg for MP4 audio extraction and conversion.
  • Demucs for source separation.
  • librosa for tempo, beat, pitch, spectral, and feature extraction.
  • Essentia for richer music-information-retrieval features such as key, rhythm, descriptors, and embeddings.
  • Basic Pitch or NeuralNote for audio-to-MIDI.
  • MusicGen / AudioCraft, Stable Audio Tools, ACE-Step, or Amphion for experimentation with open or local music generation. AudioCraft includes MusicGen for text-to-music, AudioGen for sound effects, and EnCodec; current curated overviews also list ACE-Step and Stable Audio Tools as notable open or open-weight options. github
  • REAPER as a scriptable DAW endpoint; its project structure and automation fit well with generated MIDI/CSV/JSON analysis artifacts.

A useful project structure could be:

music-ai-project/
  input/
    source.mp4
  extracted/
    source.wav
  stems/
    vocals.wav
    drums.wav
    bass.wav
    other.wav
  analysis/
    beats.json
    tempo_key.json
    chords.json
    sections.json
  midi/
    melody.mid
    bass.mid
    harmony.mid
  daw/
    orchestration.rpp
  renders/
    orchestral_mockup.wav

Important limitations

  • Source separation is lossy. Reverb, distortion, dense harmony, doubled instruments, and stereo effects cause bleed and artifacts. Treat separated material as a guide or remix asset, not a clean original recording.
  • Audio-to-MIDI is uncertain for polyphonic mixes. It works best for monophonic vocals, bass, piano with clean recording, and isolated stems; it is much weaker for a full mastered mix.
  • AI orchestration can sound generic. Prompt-based generators often produce plausible surface sound but weak long-range development, unstable instrument identity, and awkward voice leading.
  • Prompt similarity is risky. Do not ask a model to imitate a living artist or reconstruct a copyrighted recording. Use high-level descriptors—instrumentation, tempo, mood, era, structure—and write/revise the musical material yourself.
  • Licensing matters. Check the specific platform’s current commercial-use, training-data, ownership, attribution, and stem-export terms before publishing or monetizing anything. Listings commonly distinguish free tiers from paid commercial-use rights. ithub.global.ssl.fastly

A strong first experiment

Start with a short 30–60 second MP4 excerpt that you have the right to use:

  1. Extract it with FFmpeg.
  2. Run Demucs to obtain vocals/drums/bass/other.
  3. Detect BPM/key and create a beat grid with librosa.
  4. Transcribe the melody or bass stem to MIDI.
  5. In a DAW, recreate the harmonic skeleton with piano first.
  6. Orchestrate only eight tracks: strings, flute, clarinet, horns, low brass, timpani, percussion, and optional choir/pad.
  7. Render a short cue, compare it against the reference, and iterate on arrangement—not only sound design.

That approach teaches the most transferable skills: music-information retrieval, stem workflows, MIDI repair, arrangement, orchestration, and DAW automation.