HackerPapers

arXiv:1711.10958cs.SD

Now Playing: Continuous low-power music recognition

Blaise Agüera y Arcas, Beat Gfeller, Ruiqi Guo, Kevin Kilgour, Sanjiv Kumar, James Lyon, Julian Odell, Marvin Ritter, Dominik Roblek, Matthew Sharifi, Mihajlo Velimirović

Early discovery. Linked by one identifiable commenter. This paper is outside the main ranking; a mention is not an endorsement.

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.

arXiv comments
Authors are listed in alphabetical order by last name
Journal reference
DOI
Subjects
cs.SDSound cs.AIArtificial Intelligence eess.ASAudio and Speech Processing

On Hacker News

Linked by 1 identifiable commenter in 1 comment across 1 thread, from Aug 2022 to Aug 2022.

Comments linking this paper, per year.

Discussed in comments1 thread

  1. Shazam turns 20 1 comment
    • Shazam always blows my mind. It doesn't work 100% of the times, but when it does it feels like magic. On top of that they introduced (I don't know exactly when) the feature to see lyrics for the song which are automatically synched with the music. This is also mind-blowing. Only…

      ksala_View on HNParent context

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.