Sequences of feature vectors are a natural way of representing temporal data. Given a
database of sequences, a fundamental task is to find the database entry which is the most …

In this paper we present Aligned Scores and Performances (ASAP): a new dataset of 222
digital musical scores aligned with 1068 performances (more than 92 hours) of Western …

Music is present in everyday life and used for a wide range of objectives. Musical databases
have considerably increased in number and size over the past years, therefore, the …

ABSTRACT jSymbolic is an open-source platform for extracting features from symbolic
music. These features can serve as inputs to machine learning algorithms, or they can be …

Music is a type of time-series data. As the size of the data increases, it is a challenge to build
robust music genre classification systems from massive amounts of music data. Robust …

With unlabeled music data widely available, it is necessary to build an unsupervised latent
music representation extractor to improve the performance of classification models. This …

An important problem in large symbolic music collections is the low availability of high-
quality metadata, which is essential for various information retrieval tasks. Traditionally …

Computers have been used to analyze and create music since they were first introduced in
the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale …

ABSTRACT MIDI files abound and provide a bounty of information for music informatics. We
enumerate the types of information available in MIDI files and describe the steps necessary …

Deep learning models have become a critical tool for analysis and classification of musical
data. These models operate either on the audio signal, eg waveform or spectrogram, or on a …