Predicting drum beats from high-density Brain Rhythms

Entrainment is a phenomenon of phase or temporal matching of one system with that of another system. Human neural activity has been shown to resonate with external auditory stimuli. When we enjoy a piece of music, there is a resonance of brain responses with auditory signals. The crux of music cognition is based on this resonance of musical...

Increasing trust in complex machine learning systems: Studies in the music domain

Machine learning (ML) has become a core technology for many real-world applications. Modern ML models are applied to unprecedentedly complex and difficult challenges, including very large and subjective problems. For instance, applications towards multimedia understanding have been advanced substantially. Here, it is already prevalent that...

Music, Computing, and Health: A Roadmap for the Current and Future Roles of Music Technology for Health Care and Well-Being

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the...

Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings

Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time,...

One deep music representation to rule them all? A comparative analysis of different representation learning strategies

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 learning in an effective, but also efficient manner, deep transfer learning has become a common approach...

Transfer Learning of Artist Group Factors to Musical Genre Classification

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a...

Music in newspapers: Interdisciplinary opportunities and data-related challenges

Over the past millennia, music has actively been performed and listened to by mankind, thus also playing an important role in establishing sociocultural identities that have evolved over time. In parallel, for many centuries, newspapers played an important role in informing society on a regular and frequent basis on topics noteworthy at that...

Music Information Retrieval beyond Audio: A Vision-based Approach for Real-world Data

Digital music platforms have recently become the primary revenue stream for recorded music, making record labels and content owners increasingly interested in developing new digital features for their users.<br/>Besides listening to expert-curated playlists and automatically recommended music, users can also benefit from a more informative, non...

Multifaceted Approaches to Music Information Retrieval

Music is a multifaceted phenomenon: beyond addressing our auditory channel, the consumption of music triggers further senses. Also in creating and communicating music, multiple modalities are at play. Next to this, it allows for various ways of interpretation: the same musical piece can be performed in different valid ways, and audiences can in...

Mass Media Musical Meaning: Opportunities from the Collaborative Web

In the digital domain, music is usually studied from a positivist viewpoint, focusing on general ‘objective’ music descriptors. In this work, we strive to put music in a more social and cultural context, looking into ways to unify data analysis methods with thoughts from the humanities on musical meaning and significance. More specifically, we...