Audio fingerprint hierarchy searching strategies on GPGPU massively parallel computer

Abstract

Audio fingerprint was developed for representing the audio based on the content of waveform. With the audio fingerprint database, we can easily manage the song/music with high reliability and flexibility. However, with the well-developed Internet of today, the audio data have become bigger and bigger which make the management of audio/music data more difficult. There are two problems that we need to solve when the audio fingerprint database turn into bigdata: the size of the database needs to be sufficient for storing 10 millions of audio fingerprint and the strategies for searching the nearest song in acceptable time for thousands of queries at once [Nguyen Mau, T., & Inoguchi, Y. (2016). Audio fingerprint hierarchy searching on massively parallel with multi-gpgpus using K-modes and lsh. Eighth international conference on knowledge and systems engineering (KSE) (pp. 49–54). IEEE]. In this research, we propose the methods for storing the audio fingerprint using multiple GPGPU and nearest song searching strategies based on these databases. We also showed that our methods have the significant result for deploying the real system in the future.

Keywords

• Audio fingerprint
• massively parallel
• parallel processing
• GPGPU
• compute unified device architecture (CUDA)
• K-modes
• K-means
• locality sensitive hashing (LSH)