Now Playing: Continuous low-power music recognition

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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References in corpus (3)

• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
• Simultaneous Feature Learning and Hash Coding with Deep Neural Networks
• Quantization based Fast Inner Product Search

Cited by in corpus (5)

• Contrastive Unsupervised Learning for Audio Fingerprinting
• Efficient similar waveform search using short binary codes obtained through a deep hashing technique
• A Cross-Verification Approach for Protecting World Leaders from Fake and Tampered Audio
• Application of Audio Fingerprinting Techniques for Real-Time Scalable Speech Retrieval and Speech Clusterization
• Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning