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Bioimpedance is a powerful modality to continuously and non-invasively monitor cardiovascular and respiratory health parameters through the wearable operation. However, for bioimpedance sensors to be utilized in medical-grade settings, the reliability and robustness of the system should be improved. Previous studies provide limited fundamental analyses of the factors involved in the system that impact the sensitivity and the specificity of the modality in capturing the hemodynamics.

We investigate unsupervised learning of correspondences between sound events and textual phrases through aligning audio clips with textual captions describing the content of a whole audio clip. We align originally unaligned and unannotated audio clips and their captions by scoring the similarities between audio frames and words, as encoded by modality-specific encoders and using a ranking-loss criterion to optimize the model.

We investigate which loss functions provide better separations via
benchmarking an extensive set of those for music source separation.
To that end, we first survey the most representative audio source
separation losses we identified, to later consistently benchmark them
in a controlled experimental setup. We also explore using such losses
as evaluation metrics, via cross-correlating them with the results of
a subjective test. Based on the observation that the standard signal-
to-distortion ratio metric can be misleading in some scenarios, we

Measuring personal head-related transfer functions (HRTFs) is essential in binaural audio. Personal HRTFs are not only required for binaural rendering and for loudspeaker-based binaural reproduction using crosstalk cancellation, but they also serve as a basis for data-driven HRTF individualization techniques and psychoacoustic experiments. Although many attempts have been made to expedite HRTF measurements, the rotational velocities in today’s measurement systems remain lower than those in natural head movements.

In many practical parameter estimation problems,
such as coefficient estimation of polynomial regression, the true
model is unknown and thus, a model selection step is performed
prior to estimation. The data-based model selection step affects
the subsequent estimation. In particular, the oracle Cramér-Rao
bound (CRB), which is based on knowledge of the true model, is
inappropriate for post-model-selection performance analysis and
system design outside the asymptotic region. In this paper, we