Abstract
We present here the potential synergies between a sub-discipline of music called computational musicology and its associated toolkit Music Information Retrieval (MIR) systems and the current needs of soundscape ecologists who use passive acoustic recorders to collect massive amounts of audio data. These cross-sub-disciplinary synergies fall into three broad methodological categories: (1) automated feature extraction, (2) sound classification and labeling using machine learning, and (3) data visualization. We argue that the use of the MIR toolkit by soundscape ecologists could represent the development of a second generation of soundscape metrics, which are more translatable to policy and natural resource management problems. Two companion articles in this special issue by the authors provide a more detailed examination of the potential for computational musicology's MIR toolkit as applied to questions common to the emerging field of soundscape ecology.
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