Date: 5 September 2022, 2pm
Location: Seminar Room (Ground floor), Acoustics Research Institute (ÖAW), Wohllebengasse 12-14, 1040 Wien
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Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales. It offers an idealized model of spectrotemporal receptive fields (STRF) in the primary auditory cortex, and thus may serve as a biological plausible surrogate for human perceptual judgments at the scale of isolated audio events. Yet, prior implementations of JTFS and STRF have remained outside of the standard toolkit of perceptual similarity measures and evaluation methods for audio generation.
We trace this issue down to three limitations: differentiability, speed, and flexibility (co-authors: John Muradeli, Changhong Wang, Han Han, Vincent Lostanlen, Mathieu Lagrange and George Fazekas). We present an implementation of time-frequency scattering in Kymatio, an open-source Python package for scattering transforms. Unlike prior implementations, Kymatio accommodates NumPy and PyTorch as backends and is thus portable on both CPU and GPU. We demonstrate the usefulness of JTFS in Kymatio via three applications: unsupervised manifold learning of spectrotemporal modulations, supervised classification of musical instruments, and texture resynthesis of bioacoustic sounds.