MusicBrainz Picard: Lookup or Scan?

MusicBrainz Picard offers two principal methods for associating your audio files with accurate album and track metadata: Lookup and Scan. Each method targets specific file scenarios and types of embedded evidence, so understanding when to use each is key to tagging your music accurately and efficiently.

Which method — quick decision guide

1. CD or ripper log available: Select Lookup CD or Ripper Log. Picard will read the Table of Contents (TOC) and offer matching releases from MusicBrainz, ideal for files sourced directly from physical media with a rip log.
2. iTunes CDDB tag present: If you find the itunes_cddb_1 tag in your files, choose Lookup iTunes Tag. Picard reads this tag to reconstruct a TOC-like signature, narrowing its search to relevant releases.
3. Files grouped by album (clustered): Use Lookup Files. Cluster grouped files using Picard, then match the entire set to a release, which helps maintain correct album-level metadata and reduces errors.
4. Ungrouped files or questionable tags: Apply Scan Files (AcoustID fingerprinting). This analyzes audio fingerprints, matching individual tracks to recordings even if existing tags are incorrect or absent.
5. No match found by the above: Initiate a browser-based search from Picard, or manually select candidate releases on the MusicBrainz site. This is a fallback if automated identification fails.

Choose by available evidence (practical criteria)

• TOC-quality evidence present: If your files came with a CD, ripper log, or itunes_cddb_1 tag, always prefer TOC-based Lookup for best results.
• Clustered files: Lookup Files is ideal for files already grouped into albums — it preserves album relationships and supports batch processing.
• Loose, single tracks or mixes: With shuffled or mixed-origin files, Scan Files (AcoustID) provides track-level recognition without needing reliable tags or predetermined groupings.
• Mixed metadata confidence: Use Lookup Files for clusters, then Scan only those tracks that fail to match or seem anomalous. This hybrid approach reduces mistakes in complex folders.

Step-by-step workflow to avoid wrong releases

1. Test on a small sample: Duplicate one album and test Picard’s output before committing to batch edits, protecting your original files.
2. Select method according to file evidence: Refer to the quick decision guide above to choose Lookup or Scan.
3. Review candidate releases: Don’t rely on Picard’s top suggestion. Open the candidate release in MusicBrainz from within Picard (right panel browser) to verify track list, order, durations, artist, and release type.
4. Check for mismatches: Compare the number of tracks and sequence with your files. Look for variations like bonus tracks, regional editions, or reissues.
5. For Scan mode: Cross-check AcoustID matches for all tracks. Favor releases where most tracks align, not scattered matches from unrelated albums.
6. Confirm before saving: In Picard’s right-hand pane, preview reassigned metadata, ensuring title, artist, and release date match your expectations prior to writing tags.

Verification checks to run in Picard

• Verify that track durations and their order in the release match your files.
• Check that recording identifiers (IDs or names) correspond to selected album information.
• Assess for extra tracks attributable to expanded, reissued, or remastered editions, and decide whether to tag as the original or a specific edition.
• When clustering, prioritize releases that match the entire group over partial matches for a few files.

Example — fictional album with mixed evidence

Scenario: You import a folder of 12 MP3s, with disordered tags but consistent filenames. One file contains an itunes_cddb_1 tag. After clustering, Lookup Files returns two releases: a 2003 original and a 2012 remaster (15 tracks total, including bonuses).

Action: You check track numbers and durations in Picard. The remaster has more tracks, while durations for your files align with the original issue. Shorter edits confirm this match. You select the 2003 release, confirm metadata, and save. If several tracks remain unmatched, run Scan on those files to pin down their correct recording IDs and manually associate them with your chosen release.

Batch-edit safety and next steps

• Test first on copied albums to validate your approach before processing your full music library.
• If you face inconsistent or partial automated matches, use both grouping (cluster and Lookup) and track-level scanning, repeating as necessary for ambiguous files.
• Review Picard’s renaming and moving settings before saving if you wish to preserve file locations. For details, see Keep File Locations Unchanged in MusicBrainz Picard.
• For clarifications on MusicBrainz recording and release identifiers, consult MusicBrainz Recording and Release IDs Explained.

After reviewing your available evidence and selecting the relevant lookup or scan workflow, process a single album and confirm the matched release in Picard. If satisfied, extend your chosen method to similar cases for efficient, accurate batch-tagging.