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Neural networks have recently become the dominant approach to sound separation. Their good performance relies on large datasets of isolated recordings. For speech and music, isolated single channel data are readily available; however the same does not hold in the multi-channel case, and with most other sound classes. Multi-channel methods have the potential to outperform single channel approaches as they can exploit both spatial and spectral features, but the lack of training data remains a challenge. We propose unsupervised improved minimum variation distortionless response (UIMVDR), which enables multi-channel separation to leverage in-the-wild single-channel data through unsupervised training and beamforming. Results show that UIMVDR generalizes well and improves separation performance compared to supervised models, particularly in cases with limited supervised data. By using data available online, it also reduces the effort required to gather data for multi-channel approaches.

This paper presents a complete hardware and software pipeline for real-time speech enhancement in noisy and reverberant conditions. The device consists of a microphone array and a camera mounted on eyeglasses, connected to an embedded system that enhances speech and plays back the audio in headphones, with a latency of maximum 120 msec. The proposed approach relies on face detection, tracking and verification to enhance the speech of a target speaker using a beamformer and a postfiltering neural network. Results demonstrate the feasibility of the approach, and opens the door to the exploration and validation of a wide range of beamformer and speech enhancement methods for real-time speech enhancement.

Analysis of Brightness-mode ultrasound-captured fascicle angle (FA) and fascicle length (FL) can be completed manually with computer-based programs or by automated programs. Insufficient data exists regarding reliability and accuracy of automated tools. Therefore, the purpose of this study was to determine the test-retest reliability of automatic and manual ultrasound analyses, while determining accuracy of the automatic tool against the manual equivalent. Twenty-three participants (mean ± SD; age = 24 ± 4 years; height = 172.2 ± 10.5 cm; body mass = 73.1 ± 16.1 kg) completed one laboratory visit consisting of two trials where vastus lateralis muscle architecture was assessed with ultrasound. Images were taken at both lower (10 MHz) and higher frequency (12 MHz). Images were analyzed manually in an open-source imaging program and automatically using a separate open-source macro function. Test-retest reliability statistics were calculated for automatic and manual analyses. Accuracy was determined with validity statistics and were calculated for automatic analyses. The results show that manual ultrasound analyses for FA and FL for both lower and higher frequency displayed good reliability (ICC 2,1 = 0.75–0.86). However, automatic ultrasound analyses for FA and FL revealed moderate reliability (ICC 2,1 = 0.61–0.72) for the lower frequency images and poor reliability (ICC 2,1 = 0.16–0.27) for higher frequency images. When assessed against manual techniques, automatic analyses presented greater total error (TE) and standard error of the estimate (SEE) for FA at lower frequency (constant error (CE) = −3.91°, TE = 5.57°, SEE = 3.45°) than higher (CE = −2.78°, TE = −4.54°, SEE = 2.45°). For FL, the higher frequency error (CE = 0.92 cm, TE = 2.12 cm, SEE = 1.15 cm) was similar to lower frequency error (CE = 1.98 cm, TE = 3.66 cm, SEE = 1.57 cm). The findings overall show that manual analyses had good reliability and low absolute error, while demonstrating the automated counterpart had poor to moderate reliability and large errors in analyses. These findings may be impactful as they highlight the good reliability and low error associated with manually analyzed ultrasound images and validate a novel automatic tool for analyzing ultrasound images. Future work should focus on improving reliability and decreasing error in automated image analysis tools. Automated tools are promising for the field as they eliminate biases between analysts and may be more time efficient than manual techniques.

The fire service suffers from high rates of cardiovascular disease and poor overall health, and firefighters often suffer fatal and non-fatal injuries while on the job. Most fatal injuries result from sudden cardiac death, while non-fatal injuries are to the musculoskeletal system. Previous works suggest a mechanistic link between several health and performance variables and injury risk. In addition, studies have suggested physical activity and nutrition can improve overall health and occupational performance. This review offers practical applications for exercise via feasible training modalities as well as nutritional recommendations that can positively impact performance on the job. Time-efficient training modalities like high-intensity interval training and feasible modalities such as resistance training offer numerous benefits for firefighters. Also, modifying and supplementing the diet and can be advantageous for health and body composition in the fire service. Firefighters have various schedules, making it difficult for planned exercise and eating while on shift. The practical training and nutritional aspects discussed in this review can be implemented on-shift to improve the overall health and performance in firefighters.

Background/Objectives: Structured medium- and long-chain triglycerides (sMLCT) may be a superior vehicle for medium-chain fatty acid delivery to peripheral tissues, such as skeletal muscle. Limited information is available concerning the effect of sMLCT on muscular performance or recovery after a muscle-damaging exercise protocol. The purpose of this study was to establish the effect of a novel formulation of sMLCT on muscular performance and recovery. Methods: Forty female adults (mean ± SD age = 22 ± 3 years; body mass index = 23.5 ± 3.4 kg/m2) were randomized into one of two study groups, placebo control [CON; n = 20] or sMLCT [n = 20], and completed five total visits to the laboratory. The baseline (i.e., pre-exercise) assessments of muscle performance, size, and soreness were compared to assessments immediately following exercise and 24, 48, and 72 h post-exercise. Results: No statistically significant condition × time interactions were noted for strength outcomes, although trends for condition × time interactions were present for torque over 25 ms (p = 0.06) and peak torque (p = 0.05). Similarly, no condition x time interactions were present for ultrasound echo intensity, the subjective ratings of soreness and pain, thigh circumference, leg volume, and vertical jump performance. Conclusions: Within the context of the current study, the ingestion of sMLCT did not significantly influence the rate of muscle strength recovery following muscle damaging resistance exercise.

The fire service command structure encompasses recruit, incumbent firefighter, and officer positions. The purpose of this study was to quantify the effect of rank (recruits, incumbent firefighters, and officers) on health and physical ability characteristics within the fire service. Retrospective data from thirty-seven recruits (age = 29 ± 5 yrs, BMI = 26.5 ± 2.3 kg/m2); eighty-two incumbent firefighters (age = 30 ± 7 yrs, BMI = 28.8 ± 4.3 kg/m2); and forty-one officers (age = 41 ± 6 yrs, BMI = 28.6 ± 4.3 kg/m2) from a single department were used. Participants completed body composition tests (i.e., body fat percentage [%BF] and body mass index [BMI]), an air consumption test (ACT), and cardiopulmonary exercise testing. The ACT consisted of 10 standardized tasks. Five separate one-way analyses of co-variance (ANCOVA) were calculated, accounting for age. Partial eta squared statistics were calculated and Bonferroni-corrected post-hoc analyses were employed. The results demonstrated a significant effect of rank on %BF (F = 9.61, p < 0.001, η2 = 0.10); BMI (F = 3.45, p = 0.02, η2 = 0.05); relative VO2MAX (F = 12.52, p < 0.001; η2 = 0.11); and HRMAX (F = 18.89, p < 0.001, η2 = 0.03), but not on ACT time (F = 0.71, p = 0.55, η2 = 0.01). These outcomes suggest there are variations in anthropometric and physiological metrics of health across firefighter ranks. Administrators should be aware how these markers of health may vary across firefighter ranks.