Abstract: The purpose of the present study was to investigate the effectiveness of using motion graphics as a learning medium on training hearing aids maintenance skills to hearing-impaired children. The statistical population of this study consisted of all children with hearing loss in Ahvaz city, at age 4 to 7 years old. As the sample, 60, whom were selected by multistage random sampling, were randomly assigned to two groups; experimental (30 children) and control (30 children) groups. The research method was experimental and the design was pretest-posttest with the control group. The intervention consisted of a 2-minute motion graphics clip to train hearing aids maintenance skills. Data were collected using a 9-question researcher-made questionnaire. The data were analyzed by using one-way analysis of covariance. Results showed that the training of hearing aids maintenance skills with motion graphics was significantly effective for those children. The results of this study can be used by educators, teachers, professionals, and parents to train children with disabilities or normal students.
Abstract: The separation of speech signals has become a research
hotspot in the field of signal processing in recent years. It has
many applications and influences in teleconferencing, hearing aids,
speech recognition of machines and so on. The sounds received are
usually noisy. The issue of identifying the sounds of interest and
obtaining clear sounds in such an environment becomes a problem
worth exploring, that is, the problem of blind source separation.
This paper focuses on the under-determined blind source separation
(UBSS). Sparse component analysis is generally used for the problem
of under-determined blind source separation. The method is mainly
divided into two parts. Firstly, the clustering algorithm is used to
estimate the mixing matrix according to the observed signals. Then
the signal is separated based on the known mixing matrix. In this
paper, the problem of mixing matrix estimation is studied. This paper
proposes an improved algorithm to estimate the mixing matrix for
speech signals in the UBSS model. The traditional potential algorithm
is not accurate for the mixing matrix estimation, especially for low
signal-to noise ratio (SNR).In response to this problem, this paper
considers the idea of an improved potential function method to
estimate the mixing matrix. The algorithm not only avoids the inuence
of insufficient prior information in traditional clustering algorithm,
but also improves the estimation accuracy of mixing matrix. This
paper takes the mixing of four speech signals into two channels as
an example. The results of simulations show that the approach in this
paper not only improves the accuracy of estimation, but also applies
to any mixing matrix.
Abstract: Speech enhancement is a long standing problem with
numerous applications like teleconferencing, VoIP, hearing aids and
speech recognition. The motivation behind this research work is to
obtain a clean speech signal of higher quality by applying the optimal
noise cancellation technique. Real-time adaptive filtering algorithms
seem to be the best candidate among all categories of the speech
enhancement methods. In this paper, we propose a speech
enhancement method based on Recursive Least Squares (RLS)
adaptive filter of speech signals. Experiments were performed on
noisy data which was prepared by adding AWGN, Babble and Pink
noise to clean speech samples at -5dB, 0dB, 5dB and 10dB SNR
levels. We then compare the noise cancellation performance of
proposed RLS algorithm with existing NLMS algorithm in terms of
Mean Squared Error (MSE), Signal to Noise ratio (SNR) and SNR
Loss. Based on the performance evaluation, the proposed RLS
algorithm was found to be a better optimal noise cancellation
technique for speech signals.
Abstract: Bone Anchored Hearing Implants (BAHI) are
routinely used in patients with conductive or mixed hearing loss, e.g.
if conventional air conduction hearing aids cannot be used. New
sound processors and new fitting software now allow the adjustment
of parameters such as loudness compression ratios or maximum
power output separately. Today it is unclear, how the choice of these
parameters influences aided speech understanding in BAHI users.
In this prospective experimental study, the effect of varying the
compression ratio and lowering the maximum power output in a
BAHI were investigated.
Twelve experienced adult subjects with a mixed hearing loss
participated in this study. Four different compression ratios (1.0; 1.3;
1.6; 2.0) were tested along with two different maximum power output
settings, resulting in a total of eight different programs. Each
participant tested each program during two weeks. A blinded Latin
square design was used to minimize bias.
For each of the eight programs, speech understanding in quiet and
in noise was assessed. For speech in quiet, the Freiburg number test
and the Freiburg monosyllabic word test at 50, 65, and 80 dB SPL
were used. For speech in noise, the Oldenburg sentence test was
Speech understanding in quiet and in noise was improved
significantly in the aided condition in any program, when compared
to the unaided condition. However, no significant differences were
found between any of the eight programs. In contrast, on a subjective
level there was a significant preference for medium compression
ratios of 1.3 to 1.6 and higher maximum power output.
Abstract: The Siemens Healthcare Sector is one of the world's
largest suppliers to the healthcare industry and a trendsetter in
medical imaging and therapy, laboratory diagnostics, medical
information technology, and hearing aids.
Siemens offers its customers products and solutions for the entire
range of patient care from a single source – from prevention and
early detection to diagnosis, and on to treatment and aftercare. By
optimizing clinical workflows for the most common diseases,
Siemens also makes healthcare faster, better, and more cost effective.
The optimization of clinical workflows requires a
multidisciplinary focus and a collaborative approach of e.g. medical
advisors, researchers and scientists as well as healthcare economists.
This new form of collaboration brings together experts with deep
technical experience, physicians with specialized medical knowledge
as well as people with comprehensive knowledge about health
As Charles Darwin is often quoted as saying, “It is neither the
strongest of the species that survive, nor the most intelligent, but the
one most responsive to change," We believe that those who can
successfully manage this change will emerge as winners, with
valuable competitive advantage.
Current medical information and knowledge are some of the core
assets in the healthcare industry. The main issue is to connect
knowledge holders and knowledge recipients from various
disciplines efficiently in order to spread and distribute knowledge.