Thomas Wilmering

Learn More
We present a system of audio effects capable of processing and predicting metadata associated with the processed signals. This new class of audio effects has additional inputs and outputs describing features in RDF using the Audio Features Ontology. This data is processed in parallel to the audio signals. We show that by deriving the changes in metadata(More)
The Computational Analysis of the Live Music Archive (CALMA) project aims to facilitate investigation and scholarship related to live music through development of a Linked Data service combining metadata captured during deposition of audio to the Internet Archive, with computational analyses over these recordings through feature extraction, clustering, and(More)
Computational feature extraction provides one means of gathering structured analytic metadata for large media collections. We demonstrate a suite of tools we have developed that automate the process of feature extraction from audio in the Internet Archive. The system constructs an RDF description of the analysis workflow and results which is then reconciled(More)
In this paper we discuss the development of ontological representations of digital audio effects and provide a framework for the description of digital audio effects and audio effect transformations. After a brief account on our current research in the field of high-level semantics for music production using Semantic Web technologies , we detail how an(More)
In music production, descriptive terminology is used to define perceived sound transformations. By understanding the underlying statistical features associated with these descriptions, we can aid the retrieval of contextually relevant processing parameters using natural language, and create intelligent systems capable of assisting in audio engineering. In(More)
Building upon a collection with functionality for discovery and analysis has been described by Lynch as a `layered' approach to digital libraries. Meanwhile, as digital corpora have grown in size, their analysis is necessarily supplemented by automated application of computational methods, which can create layers of information as intricate and complex as(More)
This paper discusses an extension to the Audio Effect On-tology (AUFX-O) for the interdisciplinary classification of audio effect types. The ontology extension implements a unified classification system that draws on knowledge from different music-related disciplines and is designed to facilitate the retrieval of audio effect information based on low-level(More)
  • 1