Lukáš Lalinský, the founder of the Acoustid project, wrote about comparing Chromaprint fingerprints. This means there's no requirement for metadata, plus only the audio data is analysed, unlike with checksums, so the likelihood of finding a duplicate is higher.īliss uses a fingerprinting service called Acoustid (more accurately, the fingerprinter is called Chromaprint). Get fingerprintingĪudio fingerprinting is a method of producing a piece of data by analysing the actual audio data in a music file. it requires metadata! What if you have no metadata? 3. Using metadata is a good approach, but it has an obvious drawback. It's MusicBrainz works that you have to be careful with these identify a given composition, so deleting all tracks with the same works ID may lead you to deleting different versions of the same song probably not something you wanted. Duplicate recordings are often used likewise, with the additional possibility that the recording has been used in other releases too. ![]() You can try looking for MusicBrainz track, recording or work IDs which are globally unique identifiers for a particular work.Ī duplicate track ID is almost certainly a candidate for deletion, because it identifies a given song on a given release. Other tags exist that may provide an even faster result. You can normally see if a track is one of these by reviewing the containing release name. ![]() For example: live versions, acoustic versions and the like. Watch out for special versions of the tracks which you may want to keep. They may have slightly different names, but it should be a simple process of checking they are duplicates and then deleting one of them. By first organising by artist name, then track name, you are likely to be able to quickly list all duplicates together. Most tag editors allow you to sort your tracks by track name, release name and so on. By comparing your music files' internal metadata, including data such as the track name, position and containing release, you can quickly spot duplicates. ![]() In reality, checksumming is unlikely to work for detecting duplicate tracks in a music library. However, even if you could isolate the same audio data in the same format, you also have to consider the rest of the music file, comprising metadata and more. This is unlikely, given slightly different silences before and after tracks, different file formats and so on. For the checksum to produce the same result, the files must likely be exactly the same. In theory, you could run a checksum generator against every file in your music library, then compare the checksums to see which are equal, before reviewing the actual files and then deleting if they are duplicates.īut there are likely show stoppers to this approach, over and above the likelihood of a checksum clash. Importantly, different data can produce the same, clashing, checksum, but this is extremely rare (to an extent depending on the algorithm). There are many pieces of software available to generate checksums. It is normally much smaller than the source data. Use checksumsĪ checksum is a piece of data that is calculated from source data. Here are four approaches to finding duplicates. There's a brute force, but enjoyable solution: listen to all of your music! But this isn't practical and probably won't work on any non-trivial size of library. If you are sensitive to the extra storage space expended and the additional "noise" in your music library, what can you do? ![]() On the other hand, some might say duplicate tracks don't matter. One type of mistake, some might argue, is to collect duplicate music files. Four strategies to resolve duplicate music filesĪs you acquire music for your computer audio collection, from multiple sources, it's inevitable that inconsistencies are introduced and mistakes are made.
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