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This paper introduces the concept of "musical metamerism," analogous to visual metamerism, where dissimilar audio waveforms produce similar auditory sensations. The authors present a method for generating musical metamers from audio recordings using joint time-frequency scattering (JTFS) implemented in the Kymatio Python library. The method's key advantage is its lack of reliance on manual preprocessing steps like transcription or source separation.
Generate perceptually similar, yet distinct, musical excerpts without manual preprocessing using differentiable time-frequency scattering.
The concept of metamerism originates from colorimetry, where it describes a sensation of visual similarity between two colored lights despite significant differences in spectral content. Likewise, we propose to call ``musical metamerism''the sensation of auditory similarity which is elicited by two music fragments which differ in terms of underlying waveforms. In this technical report, we describe a method to generate musical metamers from any audio recording. Our method is based on joint time--frequency scattering in Kymatio, an open-source software in Python which enables GPU computing and automatic differentiation. The advantage of our method is that it does not require any manual preprocessing, such as transcription, beat tracking, or source separation. We provide a mathematical description of JTFS as well as some excerpts from the Kymatio source code. Lastly, we review the prior work on JTFS and draw connections with closely related algorithms, such as spectrotemporal receptive fields (STRF), modulation power spectra (MPS), and Gabor filterbank (GBFB).