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Concept
Music information retrieval
Summary
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.
MIR is being used by businesses and academics to categorize, manipulate and even create music.
One of the classical MIR research topic is genre classification, which is categorizing music items into one of pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, instrument identification, and music tagging are also popular topics.
Several recommender systems for music already exist, but surprisingly few are based upon MIR techniques, instead making use of similarity between users or laborious data compilation. 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.
Music source separation is about separating original signals from a mixture audio signal. Instrument recognition is about identifying the instruments involved in music. Various MIR systems have been developed that can separate music into its component tracks without access to the master copy. In this way e.g. karaoke tracks can be created from normal music tracks, though the process is not yet perfect owing to vocals occupying some of the same frequency space as the other instruments.
Automatic music transcription is the process of converting an audio recording into symbolic notation, such as a score or a . This process involves several audio analysis tasks, which may include multi-pitch detection, onset detection, duration estimation, instrument identification, and the extraction of harmonic, rhythmic or melodic information.
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Related courses (1)
DH-401: Digital musicology
This course will introduce students to the central topics in digital musicology and core theoretical approaches and methods. In the practical part, students will carry out a number of exercises.
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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 (service), Soundhound (previously Midomi), Axwave, ACRCloud and others have seen considerable success by using a simple algorithm to match an acoustic fingerprint to a song in a library. These applications take a sample clip of a song, or a user-generated melody and check a music library/music database to see where the clip matches with the song.
SoundHound
SoundHound AI, Inc. is an audio and speech recognition company founded in 2005. It develops speech recognition, natural language understanding, sound recognition and search technologies. Its featured products include Houndify, a Voice AI developer platform, SoundHound Chat AI, a voice-enabled digital assistant, and music recognition mobile app SoundHound. The company’s headquarters are in Santa Clara, California. The company was founded in 2005 by Keyvan Mohajer, an Iranian-Canadian computer scientist who had founded a number of dot com ventures before starting SoundHound.
Audio signal processing
Audio signal processing is a subfield of signal processing that is concerned with the electronic manipulation of audio signals. Audio signals are electronic representations of sound waves—longitudinal waves which travel through air, consisting of compressions and rarefactions. The energy contained in audio signals or sound level is typically measured in decibels. As audio signals may be represented in either digital or analog format, processing may occur in either domain.