Now Playing: Continuous low-power music recognition
Existing music recognition applications require a connection to a server that performs the actual recognition. In this paper we present a low-power music recognizer that runs entirely on a mobile device and automatically recognizes music without user interaction. To reduce battery consumption, a sma...
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Main Authors | , , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
29.11.2017
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Subjects | |
Online Access | Get full text |
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Abstract | Existing music recognition applications require a connection to a server that
performs the actual recognition. In this paper we present a low-power music
recognizer that runs entirely on a mobile device and automatically recognizes
music without user interaction. To reduce battery consumption, a small music
detector runs continuously on the mobile device's DSP chip and wakes up the
main application processor only when it is confident that music is present.
Once woken, the recognizer on the application processor is provided with a few
seconds of audio which is fingerprinted and compared to the stored fingerprints
in the on-device fingerprint database of tens of thousands of songs. Our
presented system, Now Playing, has a daily battery usage of less than 1% on
average, respects user privacy by running entirely on-device and can passively
recognize a wide range of music. |
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AbstractList | Existing music recognition applications require a connection to a server that
performs the actual recognition. In this paper we present a low-power music
recognizer that runs entirely on a mobile device and automatically recognizes
music without user interaction. To reduce battery consumption, a small music
detector runs continuously on the mobile device's DSP chip and wakes up the
main application processor only when it is confident that music is present.
Once woken, the recognizer on the application processor is provided with a few
seconds of audio which is fingerprinted and compared to the stored fingerprints
in the on-device fingerprint database of tens of thousands of songs. Our
presented system, Now Playing, has a daily battery usage of less than 1% on
average, respects user privacy by running entirely on-device and can passively
recognize a wide range of music. |
Author | Lyon, James Odell, Julian Gfeller, Beat Arcas, Blaise Agüera y Guo, Ruiqi Kumar, Sanjiv Kilgour, Kevin Ritter, Marvin Roblek, Dominik Velimirović, Mihajlo Sharifi, Matthew |
Author_xml | – sequence: 1 givenname: Blaise Agüera y surname: Arcas fullname: Arcas, Blaise Agüera y – sequence: 2 givenname: Beat surname: Gfeller fullname: Gfeller, Beat – sequence: 3 givenname: Ruiqi surname: Guo fullname: Guo, Ruiqi – sequence: 4 givenname: Kevin surname: Kilgour fullname: Kilgour, Kevin – sequence: 5 givenname: Sanjiv surname: Kumar fullname: Kumar, Sanjiv – sequence: 6 givenname: James surname: Lyon fullname: Lyon, James – sequence: 7 givenname: Julian surname: Odell fullname: Odell, Julian – sequence: 8 givenname: Marvin surname: Ritter fullname: Ritter, Marvin – sequence: 9 givenname: Dominik surname: Roblek fullname: Roblek, Dominik – sequence: 10 givenname: Matthew surname: Sharifi fullname: Sharifi, Matthew – sequence: 11 givenname: Mihajlo surname: Velimirović fullname: Velimirović, Mihajlo |
BackLink | https://doi.org/10.48550/arXiv.1711.10958$$DView paper in arXiv |
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Snippet | Existing music recognition applications require a connection to a server that
performs the actual recognition. In this paper we present a low-power music... |
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SubjectTerms | Computer Science - Artificial Intelligence Computer Science - Sound |
Title | Now Playing: Continuous low-power music recognition |
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