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Objectives Normalizing perceived loudness is an important rationale for gain adjustments in hearing aids. It has been demonstrated that gains required for restoring normal loudness perception for monaural narrowband signals can lead to higher-than-normal loudness in listeners with hearing loss, particularly for binaural broadband presentation. The present study presents a binaural bandwidth-adaptive dynamic compressor (BBDC) that can apply different gains for narrow- and broadband signals. It was hypothesized that normal perceived loudness for a broad variety of signals could be restored for listeners with mild to moderate high-frequency hearing loss by applying individual signal-dependent gain corrections. Design Gains to normalize perceived loudness for narrowband stimuli were assessed in 15 listeners with mild to moderate high-frequency hearing loss using categorical loudness scaling. Gains for narrowband loudness compensation were calculated and applied in a standard compressor. Aided loudness functions for signals with different bandwidths were assessed. The deviation from the average normal-hearing loudness functions was used for gain correction in the BBDC. Aided loudness functions for narrow- and broadband signals with BBDC were then assessed. Gains for a 65 dB SPL speech-shaped noise of BBDC were compared with gains based on National Acoustic Laboratories' nonlinear fitting procedure version 2 (NAL-NL2). The perceived loudness for 20 real signals was compared to the average normal-hearing rating. Results The suggested BBDC showed close-to-normal loudness functions for binaural narrow- and broadband signals for the listeners with hearing loss. Normal loudness ratings were observed for the real-world test signals. The proposed gain reduction method resulted on average in similar gains as prescribed by NAL-NL2. However, substantial gain variations compared to NAL-NL2 were observed in the data for individual listeners. Gain corrections after narrowband loudness compensation showed large interindividual differences for binaural broadband signals. Some listeners required no further gain reduction for broadband signals; for others, gains in decibels were more than halved for binaural broadband signals. Conclusion The interindividual differences of the binaural broadband gain corrections indicate that relevant information for normalizing perceived loudness of binaural broadband signals cannot be inferred from monaural narrowband loudness functions. Over-amplification can be avoided if binaural broadband measurements are included in the fitting procedure. For listeners with a high binaural broadband gain correction factor, loudness compensation for narrowband and broadband stimuli cannot be achieved by compression algorithms that disregard the bandwidth of the input signals. The suggested BBDC includes individual binaural broadband corrections in a more appropriate way than threshold-based procedures.

Sensorineural hearing loss typically results in a steepened loudness function and a reduced dynamic range from elevated thresholds to uncomfortably loud levels for narrowband and broadband signals. Restoring narrowband loudness perception for hearing-impaired (HI) listeners can lead to overly loud perception of broadband signals and it is unclear how binaural presentation affects loudness perception in this case. Here, loudness perception quantified by categorical loudness scaling for nine normal-hearing (NH) and ten HI listeners was compared for signals with different bandwidth and different spectral shape in monaural and in binaural conditions. For the HI listeners, frequency- and level-dependent amplification was used to match the narrowband monaural loudness functions of the NH listeners. The average loudness functions for NH and HI listeners showed good agreement for monaural broadband signals. However, HI listeners showed substantially greater loudness for binaural broadband signals than NH listeners: on average a 14.1 dB lower level was required to reach ""very loud"" (range 30.8 to −3.7 dB). Overall, with narrowband loudness compensation, a given binaural loudness for broadband signals above ""medium loud"" was reached at systematically lower levels for HI than for NH listeners. Such increased binaural loudness summation was not found for loudness categories below ""medium loud"" or for narrowband signals. Large individual variations in the increased loudness summation were observed and could not be explained by the audiogram or the narrowband loudness functions.

Many societies across the world are confronted with demographic changes, usually related to increased life expectancy and, often, relatively low birth rates. Information and communication technologies (ICT) may contribute to adequately support senior citizens in aging societies with respect to quality of life and quality and efficiency of health care processes. For investigating and for providing answers on whether new information and communication technologies can contribute to keeping, or even improving quality of life, health and self-sufficiency in ageing societies through new ways of living and new forms of care, the Lower Saxony Research Network Design of Environments for Ageing (GAL) had been established as a five years research project, running from 2008 to 2013. Ambient-assisted living (AAL) technologies in personal and home environments were especially important. In this article we report on the GAL project, and present some of its major outcomes after five years of research. We report on major challenges and lessons learned in running and organizing such a large, inter- and multidisciplinary project and discuss GAL in the context of related research projects. With respect to research outcomes, we have, for example, learned new knowledge about multimodal and speech-based human-machine-interaction mechanisms for persons with functional restrictions, and identified new methods and developed new algorithms for identifying activities of daily life and detecting acute events, particularly falls. A total of 79 apartments of senior citizens had been equipped with specific "GAL technology", providing new insights into the use of sensor data for smart homes. Major challenges we had to face were to deal constructively with GAL's highly inter- and multidisciplinary aspects, with respect to research into GAL's application scenarios, shifting from theory and lab experimentation to field tests, and the complexity of organizing and, in our view, successfully managing such a large project. Overall it can be stated that, from our point of view, the GAL research network has been run successfully and has achieved its major research objectives. Since we now know much more on how and where to use AAL technologies for new environments of living and new forms of care, a future focus for research can now be outlined for systematically planned studies, scientifically exploring the benefits of AAL technologies for senior citizens, in particular with respect to quality of life and the quality and efficiency of health care.

Data are presented on the relation between loudness measured in categorical units (CUs) using a standardized loudness scaling method (ISO 16832, 2006) and loudness expressed as the classical standardized measures phon and sone. Based on loudness scaling of narrowband noise signals by 31 normal-hearing subjects, sound pressure levels eliciting the same categorical loudness were derived for various center frequencies. The results were comparable to the standardized equal-loudness level contours. A comparison between the loudness function in CUs at 1000 Hz and the standardized loudness function in sones indicates a cubic relation between the two loudness measures.

Subsystems for dereverberation and acoustic echo cancellation (AEC) / acoustic echo suppression (AES) are important components in high-quality hands-free telecommunication systems. This contribution describes and analyzes a combined system for dereverberation and AEC/AES. The system identification inherently achieved by the AEC/AES system is used for the design of the room impulse response (RIR) equalization filter, i.e. the listening-room compensation (LRC) system. We use complex RIR smoothing and decoupled filtered-X least-mean-squares (dFxLMS) gradient algorithm for LRC and a combined AEC/AES system for the system identification necessary for the LRC filter design. The performance of the combined system and the mutual influences of LRC and AEC/AES are analyzed.

Increase in the number of older people due to demographic changes poses great challenges to the social healthcare systems both in the Western and as well as in the Eastern countries. Support for older people by formal care givers leads to enormous temporal and personal efforts. Therefore, one of the most important goals is to increase the efficiency and effectiveness of today’s care. This can be achieved by the use of assistive technologies. These technologies are able to increase the safety of patients or to reduce the time needed for tasks that do not relate to direct interaction between the care giver and the patient. Motivated by this goal, this contribution focuses on applications of acoustic technologies to support users and care givers in ambient assisted living (AAL) scenarios. Acoustic sensors are small, unobtrusive and can be added to already existing care or living environments easily. The information gathered by the acoustic sensors can be analyzed to calculate the position of the user by localization and the context by detection and classification of acoustic events in the captured acoustic signal. By doing this, possibly dangerous situations like falls, screams or an increased amount of coughs can be detected and appropriate actions can be initialized by an intelligent autonomous system for the acoustic monitoring of older persons. The proposed system is able to reduce the false alarm rate compared to other existing and commercially available approaches that basically rely only on the acoustic level. This is due to the fact that it explicitly distinguishes between the various acoustic events and provides information on the type of emergency that has taken place. Furthermore, the position of the acoustic event can be determined as contextual information by the system that uses only the acoustic signal. By this, the position of the user is known even if she or he does not wear a localization device such as a radio-frequency identification (RFID) tag. Category: Smart and intelligent computing

Technologies for ambient assisted living (AAL) are used to increase the quality of life of older or impaired persons. This contribution discusses the utilization of automatic speech recognition (ASR) as a natural interface for control of assistive technologies in everyday life situations. We focus on the use of hands-free systems, the technical challenges for the ASR software caused by this and the benefits for older persons. Moreover, state-of-the-art approaches for improving robustness of ASR systems are presented, discussed and demonstrated by an ASR experiment.

The importance of personalized and adaptable user-interfaces has been extensively discussed (European Ambient Assisted Living Innovation Alliance, 2009; Alexandersson et al., 2009). However, it often remains unclear how to specifically implement such concepts. In the field of acoustic communication, existing models and technologies offer a wide range of possibilities. Based on these technologies, this chapter presents a concrete realization of a model-based interface in the field of acoustic human-computer interaction. The core element of the implementation is a holistic approach towards a hearing perception model, which incorporates information of the acoustic environment, the context and the user himself provides relevant information for control and adjustment of adaptable and personalized acoustic user interfaces. In principle, this way of integrating state-of-the-art technologies and models into user interfaces could be applied to other sensory perceptions as e.g. vision.

This contribution focuses on acoustic event detection and classification for monitoring of elderly people in ambient assistive living environments such as smart homes or nursing homes. We describe an autonomous system for robust detection of acoustic events in various practically relevant acoustic situations that benefits from a voice activity detection inspired preprocessing mechanism. Therefore, various already established voice activity detection schemes have been evaluated beforehand. As a specific use case, we address coughing as an acoustic event of interest which can be interpreted as an indicator for a potentially upcoming illness. After the detection of such events using a psychoacoustically motivated spectro-temporal representation (the so-called cochleogram), we forward its output to a statistical event modeling stage for automatic instantaneous emergency classification and long-term monitoring. The parameters derived by this procedure can then be used to inform medical or care-service personal.

Speech communication is the most natural form of human interaction. Communication by means of telephones, mobile phones or video-conference systems is common nowadays especially amongst younger persons. In the past years, also a growing amount of elderly people has started to extensively use communication systems since more and more people live apart from their relatives, friends or acquaintances. However, especially elderly people suffer from hearing loss, which often prevents them from using acoustic communication devices. While approximately every second European adult of age 65+ has a hearing loss that requires treatment, only the minority actually wears hearing aids for different reasons. To tackle this problem, this contribution deals with a personalized and adaptable communication system that enhances the acoustic signal and incorporates the individual hearing loss of a hearing-impaired person. By this, the typical elderly user is enabled to take part in natural communication again.

Worldwide, ageing societies are bringing challenges for independent living and healthcare. Health-enabling technologies for pervasive healthcare and sensor-enhanced health information systems offer new opportunities for care. In order to identify, implement and assess such new information and communication technologies (ICT) the 'Lower Saxony Research Network Design of Environments for Ageing' (GAL) has been launched in 2008 as interdisciplinary research project. In this publication, we inform about the goals and structure of GAL, including first outcomes, as well as to discuss the potentials and possible barriers of such highly interdisciplinary research projects in the field of health-enabling technologies for pervasive healthcare. Although GAL's high interdisciplinarity at the beginning slowed down the speed of research progress, we can now work on problems, which can hardly be solved by one or few disciplines alone. Interdisciplinary research projects on ICT in ageing societies are needed and recommended.

This contribution discusses technologies for acoustic user interaction in ambient-assisted living (AAL) scenarios. Acoustic user interfaces allow for a natural and convenient way to interact with technical systems e.g. via sound or speech presentation or via speech input by means of automatic speech recognition (ASR) as well as by detection and classification of acoustic events. Older persons targeted by AAL technologies especially need more easy-to-use methods to interact with inherently complex supporting technology. As an example we designed and evaluated an application for acoustic user interaction with a multi-media reminder and calendar system. For this purpose, mainly older participants were involved in user studies to continuously evaluate and support the development strictly following a user-centred design process. The results suggest a wide acceptance of acoustic user interfaces by older users either for controlling inherently complex AAL systems by using robu st ASR technologies or as a natural and ambient way of presenting information to the user. However, further research is needed to increase the robustness of ASR systems when using hands-free equipment, i.e. to provide a real ambient way of interaction, and to introduce personalised speech and sound presentation schemes accounting for the individual hearing capabilities and sound preferences.

This contribution presents a system, which allows for a continuous monitoring of speech intelligibility from a single microphone signal. The system accounts for the detrimental effects of environmental noise and reverberation by estimating the two relevant parameters signal-to-noise ratio and reverberation time, and feeding them to a speech intelligibility model. Due to its real-time functionality and the fact that no reference signal is required, the system offers a wide range of opportunities to monitor communication channels and control further signal enhancement mechanisms. A priori knowledge of the individual hearing loss can be used to make the system applicable also for hearing-impaired users.

The calculation of perceived loudness is an important factor in many applications such as the assessment of noise emissions. Generally, loudness of stationary sounds can be accurately predicted by existing models. For sounds with time-varying characteristics, however, there are still discrepancies between experimental data and model predictions, even with the most recent loudness models. This contribution presents a series of experiments in which loudness was measured in normal-hearing subjects with different types of realistic signals using an adaptive loudness matching procedure and categorical loudness scaling. The results of both methods indicate that loudness of speech-like signals is largely determined by the long-term spectrum, while other speech-related properties (particularly temporal modulations) play only a minor role. Loudness of speech appears to be quite robust towards even severe signal modifications, as long as the long-term spectrum is similar. In contrast, loudness of technical, strongly impulsive signals is considerably influenced by temporal modulations. For some of the signals, loudness could not be predicted by current models. Since the perceived loudness was underestimated by the models for some signals, but overestimated for other signals, a simple adjustment of the employed time constants in the temporal integration stage could not eliminate the discrepancies.

While "classical" loudness models predict loudness in sone using the concepts of Stevens' compressive power law, (subdivided) categorical loudness perception after Heller follows the compressive logarithmic Weber‐Fechner law. To bridge the gap between both approaches, this contribution reviews various steps towards a loudness model that predicts categorical loudness (in categorical units, CU) for normal and hearing‐impaired listeners for arbitrary sounds. It uses a (modified) classical loudness model for stationary signals to derive the loudness in sone and a nonlinear transformation from sone to CU. This transformation is approximated by a cubic polynomial equation wich is derived from categorical loudness data of 84 normal‐hearing subjects. The model parameters are further set to predict the standard isophones that are in good agreement with the equal loudness level contours derived from categorical loudness data. Also, the model predicts the loudness functions near threshold both for normal and hearing...