Music information retrieval—The impact of technology, crowdsourcing, big data, and the cloud in art

Abstract

The exponential growth of computer processing power, cloud data storage, and crowdsourcing model of gathering data bring new possibilities to Music Information Retrieval (MIR) field. MIR is no longer music content retrieval only; the area also comprises the discovery of expressing feelings and emotions contained in music, incorporating other than hearing modalities for helping this issue, users’ profiling, merging music with social media and qualitative recommendations in music services. Moreover, 5G telecommunications networks, characterized by “near-instant and everything in the vicinity talks with one another,” with exponentially faster download and upload speeds, may change the existing models and create a new age of interconnectedness. This paper aims at showing some of the already highly exploited technologies and crowdsourcing models applied to music processing. Several studies are discussed in details, such as, e.g., deep learning applied to music, a way to generate an expanded training sets using 2-D data such spectrograms, mel-cepstrograms, chromagrams, and waveform-based representations of the signal instead of feature vectors in machine learning, allowing to retain all nuances related musical articulation in the signal. Also, a discussion is to be outlined, expanding the issue of the impact of these new technologies on the artistic and aesthetic values of music.