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This study aims to enhance the quality of music generation using Transformers by incorporating meta-information. While Transformer-based approaches are effective at capturing long-term dependencies in musical compositions, the music they…

Is it possible use algorithms to find trends in the history of popular music? And is it possible to predict the characteristics of future music genres? In order to answer these questions, we produced a hand-crafted dataset with the intent…

Large deep-learning models for music, including those focused on learning general-purpose music audio representations, are often assumed to require substantial training data to achieve high performance. If true, this would pose challenges…

A crucial goal of funding research and development has always been to advance economic development. On this basis, a consider-able body of research undertaken with the purpose of determining what exactly constitutes economic impact and how…

The task of a music recommender system is to predict what music item a particular user would like to listen to next. This position paper discusses the main challenges of the music preference prediction task: the lack of information on the…

With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it.…

In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then…

An average adult is exposed to hundreds of digital advertisements daily (https://www.mediadynamicsinc.com/uploads/files/PR092214-Note-only-150-Ads-2mk.pdf), making the digital advertisement industry a classic example of a big-data-driven…

Musical performance requires prediction to operate instruments, to perform in groups and to improvise. In this paper, we investigate how a number of digital musical instruments (DMIs), including two of our own, have applied predictive…

This work proposes a novel feature selection algorithm to classify Songs into different groups. Classification of musical content is often a non-trivial job and still relatively less explored area. The main idea conveyed in this article is…

Predicting athletes' performance has relied mostly on statistical data. Besides the traditional data, various types of data, including video, have become available. However, it is challenging to use them for deep learning, especially when…

We analyze a dataset providing the complete information on the effective plays of thousands of music listeners during several months. Our analysis confirms a number of properties previously highlighted by research based on interviews and…

Social media expose millions of users every day to information campaigns --- some emerging organically from grassroots activity, others sustained by advertising or other coordinated efforts. These campaigns contribute to the shaping of…

Pitch and timing information work hand in hand to create a coherent piece of music; but what happens when this information goes against the norm? Relationships between musical expectancy and emotional responses were investigated in a study…

In the modern era where highly-commodified cultural products compete heavily for mass consumption, finding the principles behind the complex process of how successful, "hit" products emerge remains a vital scientific goal that requires an…

With the continuous increase of internet usage in todays time, everyone is influenced by this source of the power of technology. Due to this, the rise of applications and games Is unstoppable. A major percentage of our population uses these…

The ubiquity of digital music consumption has made it possible to extract information about modern music that allows us to perform large scale analysis of stylistic change over time. In order to uncover underlying patterns in cultural…

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep…

Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds…

We introduce a data-driven approach to automatic pitch correction of solo singing performances. The proposed approach predicts note-wise pitch shifts from the relationship between the respective spectrograms of the singing and…