Efficient music identification using ORB descriptors of the spectrogram image

Abstract

Audio fingerprinting has been an active research field typically used for music identification. Robust audio fingerprinting technology is used to successfully perform content-based audio identification regardless of the audio signal being subjected to various types of distortion. These distortions affect the time-frequency correlation relating to pitch and speed changes. In this paper, experiments are done using the computer vision technique ORB (Oriented FAST and Rotated BRIEF) for robust audio identification. Investigations are conducted for ORB, relating to its advantage of robustness against distortions including speed and pitch changes. The ORB prototype compares the features of the spectrogram image query to a database of spectrogram images of the songs. For the initial experiment, a Brute-Force matcher is used to compare the ORB descriptors. Results show that the ORB prototype performs robustly to real-world distortions with fast, reliable performance against distortions such as speed and pitch which justifies the research done.

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“…Music audio identification systems have evolved significantly, progressing from direct hash comparisons to enhanced robustness against various audio distortions [6]. Originally leveraging domain knowledge in music and signal processing concepts for audio fingerprinting algorithms [7], [8], contemporary approaches integrate advanced machine learning [9]- [13] and computer vision techniques [4], [14]- [18], yielding highly robust music identification systems.…”

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