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The concept of a soundscape is found in both ecology and music studies. Nature soundscapes and soundscape compositions are analyzed by both disciplines, respectively, to understand their biological diversity and ecosystem functioning and to interpret their compositional structure. A major challenge for both disciplines is visualizing the information embedded in a large variety of soundscapes and to share it with different audiences, from non-professionals to experts. To analyze soundscapes, both disciplines have independently developed similarity visualizations. However, no attempt has been made yet to combine these two fields of research to improve our ecological and musical perception of environmental sounds through shared similarity analysis methods. In this paper, we introduce a new visualization tool, the soundscape chord diagram (SCD), a circular similarity representation method that can be applied to any type of soundscape, either in ecoacoustics or electroacoustic studies. Our approach consists of visualizing spectral similarities between predefined sound segments based on the computation of a b-diversity acoustic index and on automatic clustering. SCDs were tested on two ecoacoustic forest databases and two electroacoustic soundscape compositions. SCDs were performant for the identification of specific acoustic events and highlighted known diel periods for nature soundscapes and written parts for soundscape compositions. This new visualization tool allows us to easily decipher the structure of musical and ecological acoustic data. SCDs could be applied to a large variety of soundscapes and promote their knowledge and preservation. This study opens a new way of investigating soundscapes at the interface between ecology and music, bringing together science and the arts. [ABSTRACT FROM AUTHOR]

The concept of a soundscape is found in both ecology and music studies. Nature soundscapes and soundscape compositions are analyzed by both disciplines, respectively, to understand their biological diversity and ecosystem functioning and to interpret their compositional structure. A major challenge for both disciplines is visualizing the information embedded in a large variety of soundscapes and to share it with different audiences, from non-professionals to experts. To analyze soundscapes, both disciplines have independently developed similarity visualizations. However, no attempt has been made yet to combine these two fields of research to improve our ecological and musical perception of environmental sounds through shared similarity analysis methods. In this paper, we introduce a new visualization tool, the soundscape chord diagram (SCD), a circular similarity representation method that can be applied to any type of soundscape, either in ecoacoustics or electroacoustic studies. Our approach consists of visualizing spectral similarities between predefined sound segments based on the computation of a β-diversity acoustic index and on automatic clustering. SCDs were tested on two ecoacoustic forest databases and two electroacoustic soundscape compositions. SCDs were performant for the identification of specific acoustic events and highlighted known diel periods for nature soundscapes and written parts for soundscape compositions. This new visualization tool allows us to easily decipher the structure of musical and ecological acoustic data. SCDs could be applied to a large variety of soundscapes and promote their knowledge and preservation. This study opens a new way of investigating soundscapes at the interface between ecology and music, bringing together science and the arts.

This article provides a reply to 'Lure(d) into listening: The potential of cognition-based music information retrieval,' in which Henkjan Honing discusses the potential impact of his proposed Listen, Lure & Locate project on Music Information Retrieval (MIR). Honing presents some critical remarks on data-oriented approaches in MIR, which we endorse. To place these remarks in context, we first give a brief overview of the state of the art of MIR research. Then we present a series of arguments that show why purely data-oriented approaches are unlikely to take MIR research and applications to a more advanced level. Next, we propose our view on MIR research, in which the modelling of musical knowledge has a central role. Finally, we elaborate on the ideas in Honing's paper from a MIR perspective in this paper and propose some additions to the Listen, Lure & Locate project.

This article provides a reply to „Lure(d) into listening: The potential of cognition-based music information retrieval,? in which Henkjan Honing discusses the potential impact of his proposed Listen, Lure & Locate project on Music Information Retrieval (MIR). Honing presents some critical remarks on data-oriented approaches in MIR, which we endorse. To place these remarks in context, we first give a brief overview of the state of the art of MIR research. Then we present a series of arguments that show why purely data-oriented approaches are unlikely to take MIR research and applications to a more advanced level. Next, we propose our view on MIR research, in which the modelling of musical knowledge has a central role. Finally, we elaborate on the ideas in Honing?s paper from a MIR perspective in this paper and propose some additions to the Listen, Lure & Locate project. [ABSTRACT FROM AUTHOR]