Thomas Wilmering

Semantic Description of Timbral Transformations in Music Production

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 this study, we present an analysis of a dataset containing descriptive terms gathered using a series of processing modules, embedded within a Digital Audio Workstation. By applying hierarchical clustering to the audio feature space, we show that similarity in term representations exists within and between transformation classes. Furthermore, the organisation of terms in low-dimensional timbre space can be explained using perceptual concepts such as size and dissonance. We conclude by performing Latent Semantic Indexing to show that similar groupings exist based on term frequency.

Automating Annotation of Media with Linked Data Workflows

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 and combined with Linked Data about the recorded performance. This Linked Data and provenance information provides the bridging information necessary to employ analytic output in the generation of structured metadata for the underlying media files, with all data published within the same description framework.

Linked Data Publication of Live Music Archives and Analyses

We describe the publication of a linked data set exposing metadata from the Internet Archive Live Music Archive along with detailed feature analysis data of the audio files contained in the archive. The collection is linked to existing musical and geographical resources allowing for the extraction of useful or nteresting subsets of data using additional metadata.

AUFX-O: Novel Methods for the Representation of Audio Processing Workflows

This paper introduces the Audio Effect Ontology (AUFX-O) building on previous theoretical models describing audio processing units and workflows in the context of music production. We discuss important conceptualisations of different abstraction layers, their necessity to successfully model audio effects, and their application method. We present use cases concerning the use of effects in music production projects and the creation of audio effect metadata facilitating a linked data service exposing information about effect implementations. By doing so, we show how our model facilitates knowledge sharing, reproducibility and analysis of audio production workflows.