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Abstract Selective attention is a crucial ability of the auditory system. Computationally, following an auditory object can be illustrated as tracking its acoustic properties, e.g., pitch, timbre, or location in space. The difficulty is related to the fact that in a complex auditory scene, the information about the tracked object is not available in a clean form. The more cluttered the sound mixture, the more time and frequency regions where the object of interest is masked by other sound sources. How does the auditory system recognize and follow acoustic objects based on this fragmentary information? Numerous studies highlight the crucial role of top-down processing in this task. Having in mind both auditory modeling and signal processing applications, we investigated how computational methods with and without top-down processing deal with increasing sparsity of the auditory features in the task of estimating instantaneous voice states, defined as a combination of three parameters: fundamental frequency F0 and formant frequencies F1 and F2. We found that the benefit from top-down processing grows with increasing sparseness of the auditory data.

Recent advancements in neuroscientific research and miniaturized ear-electroencephalography (EEG) technologies have led to the idea of employing brain signals as additional input to hearing aid algorithms. The information acquired through EEG could potentially be used to control the audio signal processing of the hearing aid or to monitor communication-related physiological factors. In previous work, we implemented a research platform to develop methods that utilize EEG in combination with a hearing device. The setup combines currently available mobile EEG hardware and the so-called Portable Hearing Laboratory (PHL), which can fully replicate a complete hearing aid. Audio and EEG data are synchronized using the Lab Streaming Layer (LSL) framework. In this study, we evaluated the setup in three scenarios focusing particularly on the alignment of audio and EEG data. In Scenario I, we measured the latency between software event markers and actual audio playback of the PHL. In Scenario II, we measured the latency between an analog input signal and the sampled data stream of the EEG system. In Scenario III, we measured the latency in the whole setup as it would be used in a real EEG experiment. The results of Scenario I showed a jitter (standard deviation of trial latencies) of below 0.1 ms. The jitter in Scenarios II and III was around 3 ms in both cases. The results suggest that the increased jitter compared to Scenario I can be attributed to the EEG system. Overall, the findings show that the measurement setup can time-accurately present acoustic stimuli while generating LSL data streams over multiple hours of playback. Further, the setup can capture the audio and EEG LSL streams with sufficient temporal accuracy to extract event-related potentials from EEG signals. We conclude that our setup is suitable for studying closed-loop EEG & audio applications for future hearing aids.

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open Master Hearing Aid (openMHA) was developed and provided to the hearing aid research community as an open-source software platform with the aim to support sustainable and reproducible research towards improvement and new types of assistive hearing systems not limited by proprietary software. The software offers a flexible framework that allows the users to conduct hearing aid research using tools and a number of signal processing plugins provided with the software as well as the implementation of own methods. The openMHA software is independent of a specific hardware and supports Linux, macOS and Windows operating systems as well as 32-bit and 64-bit ARM-based architectures such as used in small portable integrated systems. www.openmha.org

Hearing loss can negatively influence the spatial hearing abilities of hearing-impaired listeners, not only in static but also in dynamic auditory environments. Therefore, ways of addressing these deficits with advanced hearing aid algorithms need to be investigated. In a previous study based on virtual acoustics and a computer simulation of different bilateral hearing aid fittings, we investigated auditory source movement detectability in older hearing- impaired (OHI) listeners. We found that two directional processing algorithms could substantially improve the detectability of left-right and near-far source movements in the presence of reverberation and multiple interfering sounds. In the current study, we carried out similar measurements with a loudspeaker-based setup and wearable hearing aids. We fitted a group of 15 OHI listeners with bilateral behind-the-ear devices that were programmed to have three different directional processing settings. Apart from source movement detectability, we assessed two other aspects of spatial awareness perception. Using a street scene with up to five environmental sound sources, the participants had to count the number of presented sources or to indicate the movement direction of a single target signal. The data analyses showed a clear influence of the number of concurrent sound sources and the starting position of the moving target signal on the participants’ performance, but no influence of the different hearing aid settings. Complementary artificial head recordings showed that the acoustic differences between the three hearing aid settings were rather small. Another explanation for the lack of effects of the tested hearing aid settings could be that the simulated street scenario was not sufficiently sensitive. Possible ways of improving the sensitivity of the laboratory measures while maintaining high ecological validity and complexity are discussed.

Hearing-impaired listeners are known to have difficulties not only with understanding speech in noise but also with judging source distance and movement, and these deficits are related to perceived handicap. It is possible that the perception of spatially dynamic sounds can be improved with hearing aids (HAs), but so far this has not been investigated. In a previous study, older hearing-impaired listeners showed poorer detectability for virtual left-right (angular) and near-far (radial) source movements due to lateral interfering sounds and reverberation, respectively. In the current study, potential ways of improving these deficits with HAs were explored. Using stimuli very similar to before, detailed acoustic analyses were carried out to examine the influence of different HA algorithms for suppressing noise and reverberation on the acoustic cues previously shown to be associated with source movement detectability. For an algorithm that combined unilateral directional microphones with binaural coherence-based noise reduction and for a bilateral beamformer with binaural cue preservation, movement-induced changes in spectral coloration, signal-to-noise ratio, and direct-to-reverberant energy ratio were greater compared with no HA processing. To evaluate these two algorithms perceptually, aided measurements of angular and radial source movement detectability were performed with 20 older hearing-impaired listeners. The analyses showed that, in the presence of concurrent interfering sounds and reverberation, the bilateral beamformer could restore source movement detectability in both spatial dimensions, whereas the other algorithm only improved detectability in the near-far dimension. Together, these results provide a basis for improving the detectability of spatially dynamic sounds with HAs.

The relation of the individual speech-in-noise performance differences in cochlear implant (CI) users to underlying physiological factors is currently poorly understood. This study approached this research question by a step-wise individualization of a computer model of speech intelligibility mimicking the details of CI signal processing and some details of the physiology present in CI users. Two factors, the electrical field spatial spread and internal noise (as a coarse model of the individual cognitive performance) were incorporated. Internal representations of speech-in-noise mixtures calculated by the model were classified using an automatic speech recognizer backend employing Hidden Markov Models with a Gaussian probability distribution. One-dimensional electric field spatial spread functions were inferred from electrical field imaging data of 14 CI users. Simplified assumptions of homogenously distributed auditory nerve fibers along the cochlear array and equal distance between electrode array and nerve tissue were assumed in the model. Internal noise, whose standard deviation was adjusted based on either anamnesis data, or text-reception-threshold data, or a combination thereof, was applied to the internal representations before classification. A systematic model evaluation showed that predicted speech-reception-thresholds (SRTs) in stationary noise improved (decreased) with decreasing internal noise standard deviation and with narrower electric field spatial spreads. The model version that was individualized to actual listeners using internal noise alone (containing average spatial spread) showed significant correlations to measured SRTs, reflecting the high correlation of the text-reception threshold data with SRTs. However, neither individualization to spatial spread functions alone, nor a combined individualization based on spatial spread functions and internal noise standard deviation did produce significant correlations with measured SRTs.

In contrast to static sounds, spatially dynamic sounds have received little attention in psychoacoustic research so far. This holds true especially for acoustically complex (reverberant, multisource) conditions and impaired hearing. The current study therefore investigated the influence of reverberation and the number of concurrent sound sources on source movement detection in young normal-hearing (YNH) and elderly hearing-impaired (EHI) listeners. A listening environment based on natural environmental sounds was simulated using virtual acoustics and rendered over headphones. Both near-far (‘radial’) and left-right (‘angular’) movements of a frontal target source were considered. The acoustic complexity was varied by adding static lateral distractor sound sources as well as reverberation. Acoustic analyses confirmed the expected changes in stimulus features that are thought to underlie radial and angular source movements under anechoic conditions and suggested a special role of monaural spectral changes under reverberant conditions. Analyses of the detection thresholds showed that, with the exception of the single-source scenarios, the EHI group was less sensitive to source movements than the YNH group, despite adequate stimulus audibility. Adding static sound sources clearly impaired the detectability of angular source movements for the EHI (but not the YNH) group. Reverberation, on the other hand, clearly impaired radial source movement detection for the EHI (but not the YNH) listeners. These results illustrate the feasibility of studying factors related to auditory movement perception with the help of the developed test setup.

In many daily life communication situations, several sound sources are simultaneously active. While normal-hearing listeners can easily distinguish the target sound source from interfering sound sources—as long as target and interferers are spatially or spectrally separated—and concentrate on the target, hearing-impaired listeners and cochlear implant users have difficulties in making such a distinction. In this article, we propose a binaural approach composed of a spatial filter controlled by a direction-of-arrival estimator to track and enhance a moving target sound. This approach was implemented on a real-time signal processing platform enabling experiments with test subjects in situ. To evaluate the proposed method, a data set of sound signals with a single moving sound source in an anechoic diffuse noise environment was generated using virtual acoustics. The proposed steering method was compared with a fixed (nonsteering) method that enhances sound from the frontal direction in an objective evaluation and subjective experiments using this database. In both cases, the obtained results indicated a significant improvement in speech intelligibility and quality compared with the unprocessed signal. Furthermore, the proposed method outperformed the nonsteering method.

For normal-hearing listeners, speech intelligibility improves if speech and noise are spatially separated. While this spatial release from masking has already been quantified in normal-hearing listeners in many studies, it is less clear how spatial release from masking changes in cochlear implant listeners with and without access to low-frequency acoustic hearing. Spatial release from masking depends on differences in access to speech cues due to hearing status and hearing device. To investigate the influence of these factors on speech intelligibility, the present study measured speech reception thresholds in spatially separated speech and noise for 10 different listener types. A vocoder was used to simulate cochlear implant processing and low-frequency filtering was used to simulate residual low-frequency hearing. These forms of processing were combined to simulate cochlear implant listening, listening based on low-frequency residual hearing, and combinations thereof. Simulated cochlear implant users with additional low-frequency acoustic hearing showed better speech intelligibility in noise than simulated cochlear implant users without acoustic hearing and had access to more spatial speech cues (e.g., higher binaural squelch). Cochlear implant listener types showed higher spatial release from masking with bilateral access to low-frequency acoustic hearing than without. A binaural speech intelligibility model with normal binaural processing showed overall good agreement with measured speech reception thresholds, spatial release from masking, and spatial speech cues. This indicates that differences in speech cues available to listener types are sufficient to explain the changes of spatial release from masking across these simulated listener types.

In a collaborative research project, several monaural and binaural noise reduction algorithms have been comprehensively evaluated. In this article, eight selected noise reduction algorithms were assessed using instrumental measures, with a focus on the instrumental evaluation of speech intelligibility. Four distinct, reverberant scenarios were created to reflect everyday listening situations: a stationary speech-shaped noise, a multitalker babble noise, a single interfering talker, and a realistic cafeteria noise. Three instrumental measures were employed to assess predicted speech intelligibility and predicted sound quality: the intelligibility-weighted signal-to-noise ratio, the short-time objective intelligibility measure, and the perceptual evaluation of speech quality. The results show substantial improvements in predicted speech intelligibility as well as sound quality for the proposed algorithms. The evaluated coherence-based noise reduction algorithm was able to provide improvements in predicted audio signal quality. For the tested single-channel noise reduction algorithm, improvements in intelligibility-weighted signal-to-noise ratio were observed in all but the nonstationary cafeteria ambient noise scenario. Binaural minimum variance distortionless response beamforming algorithms performed particularly well in all noise scenarios.

Owing to technological progress and a growing body of clinical experience, indication criteria for cochlear implants (CI) are being extended to less severe hearing impairments. It is, therefore, worth reconsidering these indication criteria by introducing novel testing procedures. The diagnostic evidence collected will be evaluated. The investigation includes postlingually deafened adults seeking a CI. Prior to surgery, speech perception tests [Freiburg Speech Test and Oldenburg sentence (OLSA) test] were performed unaided and aided using the Oldenburg Master Hearing Aid (MHA) system. Linguistic skills were assessed with the visual Text Reception Threshold (TRT) test, and general state of health, socio-economic status (SES) and subjective hearing were evaluated through questionnaires. After surgery, the speech tests were repeated aided with a CI. To date, 97 complete data sets are available for evaluation. Statistical analyses showed significant correlations between postsurgical speech reception threshold (SRT) measured with the adaptive OLSA test and pre-surgical data such as the TRT test (r=-0.29), SES (r=-0.22) and (if available) aided SRT (r=0.53). The results suggest that new measures and setups such as the TRT test, SES and speech perception with the MHA provide valuable extra information regarding indication for CI.

The human auditory system is sensitive in detecting "mistuned" components in a harmonic complex, which do not match the frequency pattern defined by the fundamental frequency of the complex. Depending on the frequency configuration, the mistuned component may be perceptually segregated from the complex and may be heard as a separate tone. In the context of a masking experiment, mistuning a single component decreases its masked threshold. In this study we propose to quantify the ability to detect a single component for fixed amounts of mistuning by adaptively varying its level. This method produces masking release by mistuning that can be compared to other masking release effects. Detection thresholds were obtained for various frequency configurations where the target component was resolved or unresolved in the auditory system. The results from 6 normal-hearing listeners show a significant decrease of masked thresholds between harmonic and mistuned conditions in all configurations and provide evidence for the employment of different detection strategies for resolved and unresolved components. The data suggest that across-frequency processing is involved in the release from masking. The results emphasize the ability of this method to assess integrative aspects of pitch and harmonicity perception.

A concept and architecture of a personal communication system (PCS) is introduced that integrates audio communication and hearing support for the elderly and hearing-impaired through a personal hearing system (PHS). The concept envisions a central processor connected to audio headsets via a wireless body area network (WBAN). To demonstrate the concept, a prototype PCS is presented that is implemented on a netbook computer with a dedicated audio interface in combination with a mobile phone. The prototype can be used for field-testing possible applications and to reveal possibilities and limitations of the concept of integrating hearing support in consumer audio communication devices. It is shown that the prototype PCS can integrate hearing aid functionality, telephony, public announcement systems, and home entertainment. An exemplary binaural speech enhancement scheme that represents a large class of possible PHS processing schemes is shown to be compatible with the general concept. However, an analysis of hardware and software architectures shows that the implementation of a PCS on future advanced cell phone-like devices is challenging. Because of limitations in processing power, recoding of prototype implementations into fixed point arithmetic will be required and WBAN performance is still a limiting factor in terms of data rate and delay.

Fractional Fourier transform (FrFT) has been proposed to improve the time-frequency resolution in signal analysis and processing. However, selecting the FrFT transform order for the proper analysis of multicomponent signals like speech is still debated. In this work, we investigated several order adaptation methods. Firstly, FFT- and FrFT- based spectrograms of an artificially-generated vowel are compared to demonstrate the methods. Secondly, an acoustic feature set combining MFCC and FrFT is proposed, and the transform orders for the FrFT are adaptively set according to various methods based on pitch and formants. A tonal vowel discrimination test is designed to compare the performance of these methods using the feature set. The results show that the FrFT-MFCC yields a better discriminability of tones and also of vowels, especially by using multitransform-order methods. Thirdly, speech recognition experiments were conducted on the clean intervocalic English consonants provided by the Consonant Challenge. Experimental results show that the proposed features with different order adaptation methods can obtain slightly higher recognition rates compared to the reference MFCC-based recognizer.