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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
The user records a song for 10 seconds and the application creates an audio fingerprint. Shazam works by analyzing the captured sound and seeking a match based on an acoustic fingerprint in a database of millions of songs. [7] If it finds a match, it sends information such as the artist, song title, and album back to the user.
In audio search from audio, the user must play the audio of a song either with a music player, by singing or by humming to the computer microphone. Subsequently, a sound pattern, A, is derived from the audio waveform, and a frequency representation is derived from its Fourier Transform.
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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. Musipedia, on the other hand, can identify pieces of music that contain a given melody.
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Music recognition & audio based music retrieval ~40,000,000 [47] Commercially available with SDKs, APIs for file scanning, airplay monitoring, shazam-liked features Free trial available in 15 days Gracenote: Identification service for CDs and other media. ~100,000,000 [48] ~8,000,000 [48] 1 billion "submissions". [49] Quantone
Pandora, for example, uses experts to tag the music with particular qualities such as "female singer" or "strong bassline". Many other systems find users whose listening history is similar and suggests unheard music to the users from their respective collections. MIR techniques for similarity in music are now beginning to form part of such systems.