Music Information Retrieval Technology Research Articles

Personalized music teaching service recommendation based on sensor and information retrieval technology

In order to fully utilize the existing resources of music information, people have found that music information retrieval technology has important research significance for personalized music teaching. Overall, music information retrieval technology is still in the experimental exploration stage and lacks technical and practical systems. In this context, this article proposes recommendation algorithms that can effectively adjust the content of information services based on user preferences. However, recommendation algorithms are still in an immature and complex stage, and there are still issues with their accuracy. In response to the issue of recommendation accuracy, this article utilizes information retrieval technology to optimize recommendation algorithms. Combine these two algorithms for recommendation. The main steps that affect recommendation results in recommendation algorithms include similarity calculation, nearest neighbor selection method, and score prediction method calculation. The experimental results indicate that the proposed algorithm can effectively solve the existing problems of users, and can also accurately retrieve the content of interest, greatly improving the diversity of the proposed content. We use utilizing algorithmic learning to create a personalized music teaching system that enables students to independently advance towards their self-development goals, ultimately achieving the goal of promoting the harmonious development of students' personalities.

Information Retrieval Technologies and Big Data Analytics to Analyze Product Innovation in the Music Industry

In Cultural and Creative Industries, innovation contributes to generating a competitive advantage thanks to the fundamental role assumed by the human creativity and the quest for novelty. In particular, the music industry stands out as one of the most successful ones, in terms of both revenue and employment. The music industry is also quickly and constantly growing, supported by the new digital technologies and the rise of streaming platforms and digital services, which have increased the availability of continuous, reliable, and timely data. Consequently, this may allow the implementation of novel techniques to study product innovation occurring in the music industry. Nonetheless, quantitative approaches to study innovation in this industry are scant. The present study aims at filling this gap by developing a quantitative approach to analyze product innovation dynamics in the music industry exploiting data collected through Music Information Retrieval technologies. We selected a successful band as a case study and analyzed each song released from 1984 to 2016 to obtain a quantitative representation of their musical production. We then developed and applied quantitative similarity metrics to see how each album was similar or different from the previous ones and from the most relevant music genres, to better understand innovation dynamics in music production.

MPEG-7—Standardized tools for music information retrieval

Today, many applications in Music Information Retrieval (MIR) employ audio features which have been tailored individually by the algorithm developers. For a broader use also in commercial applications, MIR technology can benefit significantly from a ‘‘common language’’ in audio signal description that can be used to annotate any type of multimedia assets in order to facilitate search and retrieval according to a wide range of conceivable criteria in an interoperable way. The audio part of the ISO/MPEG-7 ‘‘Multimedia Content Description Interface’’ provides such a common signal description language by defining a rather comprehensive set of standardized features [called ‘‘low level descriptors’’ (LLDs)], application-centric subsets, and a unified way of exchanging this data based on XML. The talk provides an overview of the MPEG-7 Audio tool chest, including existing and forthcoming extensions. While the idea is clearly to create a universal platform for any conceivable MIR task, some of the initially conceived applications of MPEG-7 Audio are illustrated.