Umeå universitets logga

Objectives: While there are several consumer mobile applications for hearing assessment, only a few have been clinically studied. Nevertheless, none of them accounts for underlying hardware differences. Moreover, there is currently no app available to assess the user’s capability of resolving temporal cues (i.e. temporal resolution), which is vital for speech perception and sound localisation. To address these limitations, we developed an Android app that adapts to the frequency response of Bluetooth-enabled headphones and conducts pure-tone audiometry and a temporal masking (TM) test.

Design and Study Sample: 24 normal-hearing and 24 hearing-impaired individuals (mean age = 49.2 and 59.0 years, respectively) were recruited. Clinical audiometry was conducted according to ISO8253-:2010 at 250 to 8000 Hz. The app-based audiometry was conducted at the same frequencies. A TM paradigm was implemented and conducted at gap durations of 5,10,20,40, and 80 ms.

Results: App-based audiometry results closely matched the clinical measurements. Linear mixed models revealed no significant differences between the two methods. The correlation between the two methods was significant (p<.001) and strong (r=.90). Our TM method provided a quick estimate of auditory temporal resolution at 500, 2000, and 4000 Hz.

Conclusion: The app can accurately and quickly estimate the user’s clinical audiogram and temporal resolution.

In this paper, we present a haptic-based wireless electronic system that helps users with profound visual impairment avoid obstacles and map their surroundings through an unknown landscape. Our aim has been to design a lightweight and low-cost system that can easily and discretely be worn by the user. The system comprises two parts: 1) the “sensor module”which is laser-based and can be worn on the belt, and 2) two “haptic modules” which can be worn on each wrist with its vibrators directly placed on the skin. These two parts communicate wirelessly which makes it comfortable for the user to wear the system. The sensor module creates a representation matrix of the surrounding objects, and as an object gets closer, a higher pulse-width-modulation (PWM) duty cycle (i.e., a higher vibratorypower) is delivered by the corresponding vibrator(s), indicating its direction and proximity. This obstacle avoidance system was tested on seven individuals between 50 and 78 years of age (mean=63.5, SD=9.3 years) with profound vision impairment. The participants were instructed to walk through two path configurations: one with and another without the proposed assistive system. According to our results, significantly (p<0.05) shorter time(Median = 64s (IQR 32-81) versus Median = 95s (IQR 51-134) was needed and slightly fewer collisions occurred once they used this assistive system. The participants stated that they found the haptic signals intuitive and easy to understand. Hardware schematics and codes are publicly available for future development.

Acoustic echo cancelation (AEC) is a system identification problem that has been addressed by various techniques and most commonly by normalized least mean square (NLMS) adaptive algorithms. However, performing a successful AEC in large commercial vehicles has proved complicated due to the size and challenging variations in the acoustic characteristics of their cabins. Here, we present a wideband fully linear time domain NLMS algorithm for AEC that is enhanced by a statistical double-talk detector (DTD) and a voice activity detector (VAD). The proposed solution was tested in four main Volvo truck models, with various cabin geometries, using standard Swedish hearing-in-noise (HINT) sentences in the presence and absence of engine noise. The results show that the proposed solution achieves a high echo return loss enhancement (ERLE) of at least 25 dB with a fast convergence time, fulfilling ITU G.168 requirements. The presented solution was particularly developed to provide a practical compromise between accuracy and computational cost to allow its real-time implementation on commercial digital signal processors (DSPs). A real-time implementation of the solution was coded in C on an ARM Cortex M-7 DSP. The algorithmic latency was measured at less than 26 ms for processing each 50-ms buffer indicating the computational feasibility of the proposed solution for real-time implementation on common DSPs and embedded systems with limited computational and memory resources. MATLAB source codes and related audio files are made available online for reference and further development.