This study presents a kick drum microphone taxonomy of 46 distinct recording configurations from 32 different microphones, six of which featured varied voicing options. A range of spectral, temporal, and timefrequency features tailored to the transient and low-frequency nature of kick drums were extracted. These features were then standardised before dimensionality reduction via Principal Component Analysis (PCA). Microphones were then grouped using agglomerative hierarchical clustering with Wards method, resulting in a dendrogram that visualises timbral similarity across both fixed- and switchable-voicing models. Cophenetic distance was used to rank microphone pairings by dissimilarity, revealing that voicing options, while measurable, typically result in subtler variations than changing between different models. A subset of microphones formed a maximally distinct cluster, suggesting strong timbral deviation from the rest of the dataset. The findings support practical use cases for microphone substitution, timbral matching, and educational demonstration. Rather than ranking microphones based on perceptual quality, this work provides a reproducible, analytical framework for understanding timbral relationships for kick drum recording.
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https://aes2.org/publications/elibrary-page/?id=23043
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