Human dissimilarity ratings of musical instrument timbre: A computational meta-analysis

Musical instrument timbre has been intensively investigated through dissimilarity rating tasks. It is now well known that audio descriptors such as attack time and spectral centroid, among others, account well for the dimensions of the timbre spaces underlying these dissimilarity ratings. Nevertheless, it remains very difficult to reproduce these perceptual judgments from distances computed on acoustical representations such as the waveform or the spectrogram. Interestingly, biologically inspired representations based on spectrotemporal modulation spectra such as spectrotemporal receptive fields (STRF) have been shown to be well-suited to reproduce human dissimilarity ratings (Patil et al., 2012). Here, we propose a meta-analysis of seven former studies on timbre spaces in light of these recently developed representations. We implemented a computational framework that optimizes the correlation between the perceptual results and distances obtained from a set of different acoustic representations, in particular through the STRF. We observed that distances computed from spectrotemporal modulation representations provide the best correlation with the perceptual results across the seven timbre spaces. Finally, we highlighted the parts of the representations contributing the most to the correlation suggesting new insights into the underlying perceptual metrics. [Supported by Canada Research Chair, NSERC (RGPIN-2015-05208, RGPAS-478121-15), (RGPIN-262808-2012), and EU MSCf (Project MIM, H2020-MSCA-IF-2014, GA no. 659).]