Tag editor
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A tag editor (or tagger) is a piece of software that supports editing metadata of multimedia file formats, rather than the actual file content. These are mainly taggers for common audio tagging formats like ID3, APE, and Vorbis comments (for example Windows Media Player and iTunes), but can also be taggers for JPEG and TIFF metadata, for example (iPhoto).
A common purpose of tag editors is to correct and sort multimedia files, for example music collections. This often happens in a batch processing mode so that one doesn't have to manually edit every file on his or her own.
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[edit] Types
[edit] For song files
[edit] Manual
Some programs, such as Windows Media Player, allow users to manually edit tag and song file information, and, in the case of Windows Media Player, there are many advanced fields to edit, including composer, release year, etc., although, Windows Media Player is also a program that automatically looks up CD information.
[edit] Automatic
[edit] Comparing already-existing tag information to online music databases
One type of tag editor compares the already-existing information in a song file's tag to information from online music databases, such as freedb or MusicBrainz. If the already-existing tag information matches song information of a song from an online music database, then the tag information and information about the song (such as song name and album) can be changed according to that song information match.
[edit] Acoustic fingerprinting
[edit] Hash function
In hash function, for audio identification, such as finding out whether an MP3 file matches one of a list of known items, one could use a conventional hash function such as MD5, but this would be very sensitive to highly likely perturbations such as time-shifting, CD read errors, different compression algorithms or implementations or changes in volume. Using something like MD5 is useful as a first pass to find exactly-identical files, but another, more advanced algorithm is required to find all items that would nonetheless be interpreted as identical to a human listener. Although they are not common[citation needed], hashing algorithms do exist that are robust to these minor differences. Most of the algorithms available are not extremely robust, but some are so robust that they can identify music played on loud-speakers in a noisy room.[citation needed] One example of this in practical use is the service Shazam. Customers call a number and place their telephone near a speaker. The service then analyses the music, and compares it to known hash values in its database. The name of the music is sent to the user. An open source alternative to this service is MusicBrainz, which creates an acoustic fingerprint for an audio file and matches it to its online, community-driven database.
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