Transformers have enabled impressive improvements in deep learning. They often outperform recurrent and convolutional models in many tasks while taking advantage of parallel processing. Recently, we proposed the SepFormer, which obtains state-of-the-art performance in speech separation with the WSJ0-2/3 Mix datasets. This paper studies in-depth Transformers for speech separation. In particular, we extend our previous findings on the SepFormer by providing results on more challenging noisy and noisy-reverberant datasets, such as LibriMix, WHAM!, and WHAMR!. Moreover, we extend our model to perform speech enhancement and provide experimental evidence on denoising and dereverberation tasks. Finally, we investigate, for the first time in speech separation, the use of efficient self-attention mechanisms such as Linformers, Lonformers, and ReFormers. We found that they reduce memory requirements significantly. For example, we show that the Reformer-based attention outperforms the popular Conv-TasNet model on the WSJ0-2Mix dataset while being faster at inference and comparable in terms of memory consumption.

ManyEars is an open framework for microphone array-based audio processing. It consists of a sound source localization, tracking and separation system that can provide an enhanced speaker signal for improved speech and sound recognition in real-world settings. ManyEars software framework is composed of a portable and modular C library, along with a graphical user interface for tuning the parameters and for real-time monitoring. This paper presents the integration of the ManyEars Library with Willow Garage’s Robot Operating System. To facilitate the use of ManyEars on various robotic platforms, the paper also introduces the customized microphone board and sound card distributed as an open hardware solution for implementation of robotic audition systems.

Head pose assessment can reveal important clinical information on human motor control. Quantitative assessment have the potential to objectively evaluate head pose and movements’ specifics, in order to monitor the progression of a disease or the effectiveness of a treatment. Optoelectronic camera-based motion-capture systems, recognized as a gold standard in clinical biomechanics, have been proposed for head pose estimation. However, these systems require markers to be positioned on the person’s face which is impractical for everyday clinical practice. Furthermore, the limited access to this type of equipment and the emerging trend to assess mobility in natural environments support the development of algorithms capable of estimating head orientation using off-the-shelf sensors, such as RGB cameras. Although artificial vision is a popular field of research, limited validation of human pose estimation based on image recognition suitable for clinical applications has been performed. This paper first provides a brief review of available head pose estimation algorithms in the literature. Current state-of-the-art head pose algorithms designed to capture the facial geometry from videos, OpenFace 2.0, MediaPipe and 3DDFA_V2, are then further evaluated and compared. Accuracy is assessed by comparing both approaches to a baseline, measured with an optoelectronic camera-based motion-capture system. Results reveal a mean error lower or equal to $5.6^{\circ }$ for 3DDFA_V2 depending on the plane of movement, while the mean error reaches $14.1^{\circ }$ and $11.0^{\circ }$ for OpenFace 2.0 and MediaPipe, respectively. This demonstrates the superiority of the 3DDFA_V2 algorithm in estimating head pose, in different directions of motion, and suggests that this algorithm can be used in clinical scenarios.

Artificial audition aims at providing hearing capabilities to machines, computers and robots. Existing frameworks in robot audition offer interesting sound source localization, tracking and separation performance, although involve a significant amount of computations that limit their use on robots with embedded computing capabilities. This paper presents ODAS, the Open embeddeD Audition System framework, which includes strategies to reduce the computational load and perform robot audition tasks on low-cost embedded computing systems. It presents key features of ODAS, along with cases illustrating its uses in different robots and artificial audition applications.

Persons with heart failure (HF) at high risk for adverse events should be followed by specialized HF clinics, since follow-up by specialized HF clinics improves outcomes for HF patients. The objective was to determine whether there were disparities for gender and other factors associated with referral of patients to specialized HF clinics. In this prospective cohort study, patients with a confirmed primary diagnosis of HF were recruited by nurses at 8 hospital emergency departments (ED) in Québec, Canada. They were interviewed by telephone at 6 weeks post ED discharge and subsequently at 3 months and 6 months. Pertinent clinical variables were extracted from medical charts by trained nurses. Bivariate analysis and multiple logistic regression were used to identify whether gender and other potential factors were associated with referral to the HF clinic. We enrolled 549 patients (mean age 75.5±11.0 years; 51% males). By 6 months after their ED visit for HF, 37.6% of the cohort were referred to specialized HF clinics. Men were more likely to be referred (odds ratio [OR] 2.04; 95% confidence interval [CI] 1.12, 3.74). Other factors associated with referral were younger age (OR 0.95; 95% CI 0.92, 0.98), and systolic dysfunction HF (left ventricle ejection fraction <40%) (OR 3.08; 95% CI 1.77, 5.46). There are disparities in referral with respect to gender, age, and type of HF. These disparities in referral need to be addressed.