![]() FAST foresees that music content will be packaged in a flexible, structured way that combines audio recordings with rich, layered, standardized metadata to support interactive and adaptive musical experiences. In the future, music experiences will demand far richer musical information, that supplements digital audio. The FAST project (Fusing Audio and Semantic Technology for Intelligent Music Production and Consumption), with 5 years of UK funding, seeks to create a new technological ecosystem for recorded music that empowers people throughout the value chain, from professional performers to casual listeners, and thereby help them engage in new, more creative, immersive, and dynamic musical experiences. Music’s Changing Fast FAST Is Changing Music This contributes to curiosity-driven research about music as a fundamental human trait, paving the way for cross-disciplinary approaches to music encompassing computer science, musicology, and cognition. The explicit modeling within the computational context enhances our understanding of how we employ these concepts implicitly when interacting with music. Starting from our research on developing online search methods for Dutch folk songs and on developing online music education systems, I will demonstrate how crucial concepts, such as music similarity, harmonic variance, and repeated patterns, are scrutinized in the process of developing computational models. In this talk, I will discuss how the explicit modeling of musical structures in the computational domain uncovers layers of implicit musical knowledge applied by expert and ordinary listeners when interacting with music. At the same time, the process of developing these new technologies employing an application-oriented perspective has revealed many open questions about music as a fundamental human trait. They have enabled new ways of accessing and interacting with music. ![]() Over the past decades, we have witnessed a rapid development of music technology for many different application contexts, such as music recommender systems, music search engines, automatic music generation systems, and new interactive musical instruments. Towards Explicating Implicit Musical Knowledge: How the Computational Modeling of Musical Structures Mediates between Curiosity-Driven and Application-Oriented Perspectives ![]() Our studies have advanced the understanding of music processing in the brain, demonstrating activity in large-scale networks connecting audio-motor, emotion, and cognitive regions of the brain during the act of listening to whole pieces of music. This free-listening paradigm benefits from music information retrieval, since it handles the computationally extracted features from the music as time series variables to be related to the brain signal. In our recent studies, we introduced a novel experimental paradigm where participants are simply asked to naturalistically listen to music rather than to perform tasks in response to some artificial sounds. This controlled approach limits the generalization of findings to real-life listening situations. To investigate this, various stimulation paradigms have been developed, most of them being distant from naturalistic constantly-varying sound environments in order to maintain strict control over manipulated variables. The ways those codes for musical sounds are obtained and represented in the cerebral cortex is only partially understood. These neuronal codes are then transferred in several relay stations of the central nervous system to reach the primary and non-primary auditory cerebral cortex. The peripheral hearing apparatus has taken its shape to decompose sounds by transforming air pressure waves into ion impulses and by extracting the frequencies in a way similar to Fourier transform at the level of the basilar membrane in the inner ear. Listening to musical sounds is a brain function that has likely appeared already tens of thousands of years ago, in Homo sapiens and perhaps even in Neanderthal ancestors.
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