Machine learning techniques for automated musical genre classification is currently widely studied. With large collections of digital musical files, one approach to classification is to classify by musical genres such as pop, rock and classical in Western music. Beat, pitch and temporal related features are extracted from audio signals and various machine learning algorithms are applied for cla...

This is an extended analysis of eight different algorithms for musical tempo extraction and beat tracking. The algorithms participated in the 2006 Music Information Retrieval Evaluation eXchange (MIREX), where they were evaluated using a set of 140 musical excerpts, each with beats annotated by 40 different listeners. Performance metrics were constructed to measure the algorithms’ abilities to ...

In this paper, we discuss a design principle for the musical instruments that are useful for both novices and professional musicians and that facilitate musically rich expression. We believe that the versatility of conventional musical instruments causes difficulty in performance. By dynamically specializing a musical instrument for performing a specific (genre of) piece, the musical instrument...

Introduction The computer classification of musical audio is an important task in music information retrieval (MIR). Classification is a standard machine-learning task that typically involves predicting an output (for example, the name of an appropriate musical genre) from an input (for example, an audio file stored on a computer). Unsurprisingly, music classification is a hard task. For one th...

Music Genre Classification is one of the most active tasks in Music Information Retrieval (MIR). Many successful approaches can be found in literature. Most of them are based on Machine Learning algorithms applied to different audio features automatically computed for a specific database. But there is no computational model that explains how musical features are combined in order to yield genre...

Video games and music have influenced each other since the beginning of the consumer video game era. In particular the “chiptune” genre of music uses sounds from 8-bit video games; these sounds have even found their way into contemporary popular music. However, in this genre, game sounds are arranged using conventional musical interfaces, meaning the games themselves (their algorithms, design a...

Music genres allow to categorize musical items that share common characteristics. Although these categories are not mutually exclusive, most related research is traditionally focused on classifying tracks into a single class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for certain applications. In this work we aim to expand this task by categorizing musical items into m...

Most research on musical performance anxiety has focused on musicians coming from a classical background, and performance anxiety experiences of musicians outside the western classical genre remain under-researched. The aim of this study was to investigate perceived performance anxiety experiences in undergraduate and professional musicians and to explore whether musical genre specialization (W...

Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test t...

Audio content analysis can assist investigation of musical influence, given a corpus of date-annotated works. We study a number of techniques which illuminate musicological questions on genre and creative influence. By applying machine learning tests and statistical analysis to a database of early EDM tracks, we examine how distinct putatively different musical genres really are, the retrospect...