Diverse disciplines are interested in how the coordination of interacting agents' movements, emotions, and physiology over time impacts social behavior. Here, we describe a new multivariate procedure for automating the investigation of this kind of behaviorally-relevant “interactional synchrony”, and introduce a novel interactional synchrony measure based on features of dynamic time warping (DTW) paths. We demonstrate that our DTW path-based measure of interactional synchrony between facial action units of two people interacting freely in a natural social interaction can be used to predict how much trust they will display in a subsequent Trust Game. We also show that our approach outperforms univariate head movement models, models that consider participants' facial action units independently, and models that use previously proposed synchrony or similarity measures. The insights of this work can be applied to any research question that aims to quantify the temporal coordination of multiple signals over time, but has immediate applications in psychology, medicine, and robotics.

We develop a time-scale synthesis-based probabilistic approach for the modeling of locally stationary signals. Inspired by our previous work, the model involves zero-mean, complex Gaussian wavelet coefficients, whose distribution varies as a function of time by time dependent translations on the scale axis. In a maximum a posteriori approach, we propose an estimator for the model parameters, namely the time-varying scale translation and an underlying power spectrum. The proposed approach is illustrated on a denoising example. It is also shown that the model can handle locally stationary signals with fast frequency variations, and provide in this case very sharp time-scale representations more concentrated than synchrosqueezed or reassigned wavelet transform.

Spectral estimation generally aims at determining from a single realization of a given signal, the distribution of its power as a function of frequency. In this letter, we focus on multivariate, locally time-warped signals. We show that the spectral estimation problem can also be interpreted as a doubly nonstationary blind source separation (BSS) problem, where both the mixing matrix and the original sources contribute to nonstationarity. We then introduce a BSS algorithm for joint spectral estimation of nonstationary sources. The performance of the proposed approach is evaluated on numerical simulations, and compared with other nonstationary BSS algorithms.

A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. The focus is on time warping and amplitude modulation, and an approximate maximum-likelihood approach based on suitable approximations in the wavelet transform domain is developed. This paper provides theoretical analysis of the approximations, and introduces joint estimation of frequency, amplitude, and spectrum, a corresponding estimation algorithm. The latter is tested and validated on synthetic as well as real audio signal.