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Music information and reviews. ~20,000,000 [7] ~2,200,000 [7] Song samples only. Discogs • Database: user-generated cross-referenced database of physical & digital releases, artists, and labels. With catalogue numbers, codes, and other markings taken directly from each release.
DuckDB's SQL parser is derived from the pg_query library developed by Lukas Fittl, which is itself derived from PostgreSQL's SQL parser that has been stripped down as much as possible. [ 12 ] [ 13 ] DuckDB uses a single-file storage format to store data on disk, designed to support efficient scans and bulk updates, appends and deletes. [ 14 ]
To download a subset of the database in XML format, such as a specific category or a list of articles see: Special:Export, usage of which is described at Help:Export. Wiki front-end software: MediaWiki. Database backend software: MySQL. Image dumps: See below.
Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. Those involved in MIR may have a background in academic musicology , psychoacoustics , psychology , signal processing , informatics , machine learning , optical music recognition , computational intelligence , or some combination of these.
Musipedia's search engine works differently from that of search engines such as Shazam. The latter can identify short snippets of audio (a few seconds taken from a recording), even if it is transmitted over a phone connection. Shazam uses Audio Fingerprinting for that, a technique that makes it possible to identify recordings.
Much of the data that makes up the Wikipedia encyclopedia is stored in a SQL database. It can sometimes be useful to run queries against this database to extract information that is otherwise hard to find. For example: Articles with H.M.S. in their title that have not been edited for 12 months.
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Search by sound is the retrieval of information based on audio input. There are a handful of applications, specifically for mobile devices that utilize search by sound. Shazam, Soundhound, Axwave, ACRCloud and others have seen considerable success by using a simple algorithm to match an acoustic fingerprint to a song in a library