Visible to the public Potential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise

TitlePotential Barriers to Music Fingerprinting Algorithms in the Presence of Background Noise
Publication TypeConference Paper
Year of Publication2020
AuthorsMehmood, Z., Qazi, K. Ashfaq, Tahir, M., Yousaf, R. Muhammad, Sardaraz, M.
Conference Name2020 6th Conference on Data Science and Machine Learning Applications (CDMA)
Date PublishedMarch 2020
ISBN Number978-1-7281-2746-0
Keywordsacoustic feature modeling, Acoustic Fingerprint, Acoustic Fingerprints, Acoustic signal processing, audio sample identification, audio signal, audio signal processing, classification, composability, condensed signature, deep learning techniques, digital signature, digital signatures, Hashing curve detection, Human Behavior, iKala dataset, image processing, learning (artificial intelligence), MFP, MIR-1K dataset, music, music fingerprint classification, music fingerprinting algorithms, MusicBrainz dataset, pubcrawl, resilience, Resiliency, Shazam, signal classification, song identification

An acoustic fingerprint is a condensed and powerful digital signature of an audio signal which is used for audio sample identification. A fingerprint is the pattern of a voice or audio sample. A large number of algorithms have been developed for generating such acoustic fingerprints. These algorithms facilitate systems that perform song searching, song identification, and song duplication detection. In this study, a comprehensive and powerful survey of already developed algorithms is conducted. Four major music fingerprinting algorithms are evaluated for identifying and analyzing the potential hurdles that can affect their results. Since the background and environmental noise reduces the efficiency of music fingerprinting algorithms, behavioral analysis of fingerprinting algorithms is performed using audio samples of different languages and under different environmental conditions. The results of music fingerprint classification are more successful when deep learning techniques for classification are used. The testing of the acoustic feature modeling and music fingerprinting algorithms is performed using the standard dataset of iKala, MusicBrainz and MIR-1K.

Citation Keymehmood_potential_2020