From Imitation to Innovation: The Divergent Paths of Techno in Germany and the USA

Many documentaries on early house and techno music exist. Here, protagonists from the scenes describe key elements and events that affected the evolution of the music. In the research community, there is consensus that such descriptions have to be examined critically. Yet, there have not been attempts to validate such statements on the basis of audio analyses. In this study, over 9,000 early house and techno tracks from Germany and the United States of America are analyzed using recording studio features, machine learning and inferential statistics. Three observations can be made: 1.) German and US house/techno music are distinct, 2.) US styles are much more alike, and 3.) scarcely evolved over time compared to German house/techno regarding the recording studio features. These findings are in agreement with documented statements and thus provide an audio-based perspective on why techno became a mass phenomenon in Germany but remained a fringe phenomenon in the USA. Observations like these can help the music industry estimate whether new trends will experience a breakthrough or disappear.

Sound Terminology Describing Production and Perception of Sonification

Sonification research is intrinsically interdisciplinary. Consequently, a proper documentation of, and interdisciplinary discourse about a sonification is often hindered by terminology discrepancies between involved disciplines, i.e., the lack of a common sound terminology in sonification research. Without a common ground, a researcher from one discipline may have troubles understanding the implementation and imagining the resulting sound perception of a sonification, if the sonification is described by a researcher from another discipline. To find a common ground, I consulted literature on interdisciplinary research and discourse, identified problems that occur in sonification, and applied the recommended solutions. As a result, I recommend considering three aspects of sonification individually, namely 1.) Sound Design Concept, 2.) Objective and 3.) Method, clarifying which discipline is involved in which aspect, and sticking to this discipline's terminology. As two requirements of sonifications are that they are a) reproducible and b) interpretable, I recommend documenting and discussing every sonification design once using audio engineering terminology, and once using psychoacoustic terminology. The appendix provides comprehensive lists of sound terms from both disciplines, together with relevant literature and a clarification of often misunderstood and misused terms.

Goniometers are a Powerful Acoustic Feature for Music Information Retrieval Tasks

Goniometers, also known as Phase Scopes or Vector Scopes, are audio metering tools that help music producers and mixing engineers monitor spatial aspects of a music mix, such as the stereo panorama, the width of single sources, the amount and diffuseness of reverberation as well as phase cancellations that may occur on the sweet-spot and in a mono-mixdown. In addition, they implicitly inform about the dynamics of the sound. Self-organizing maps trained with a goniometer, are consulted to explore the usefulness of this acoustic feature for music information retrieval tasks. One can see that goniometers are able to classify different genres and cluster a single album. The advantage of goniometers is the causality: Music producers and mixing engineers consciously consult goniometers to reach their desired sound, which is not the case for other acoustic features, from Zero-Crossing Rate to Mel-Frequency Cepstral Coefficients.

Novel Recording Studio Features for Music Information Retrieval

In the recording studio, producers of Electronic Dance Music (EDM) spend more time creating, shaping, mixing and mastering sounds, than with compositional aspects or arrangement. They tune the sound by close listening and by leveraging audio metering and audio analysis tools, until they successfully creat the desired sound aesthetics. DJs of EDM tend to play sets of songs that meet their sound ideal. We therefore suggest using audio metering and monitoring tools from the recording studio to analyze EDM, instead of relying on conventional low-level audio features. We test our novel set of features by a simple classification task. We attribute songs to DJs who would play the specific song. This new set of features and the focus on DJ sets is targeted at EDM as it takes the producer and DJ culture into account. With simple dimensionality reduction and machine learning these features enable us to attribute a song to a DJ with an accuracy of 63%. The features from the audio metering and monitoring tools in the recording studio could serve for many applications in Music Information Retrieval, such as genre, style and era classification and music recommendation for both DJs and consumers of electronic dance music.