Bas van Dijk

Speech understanding in background noise with the two-microphone adaptive beamformer BEAM in the Nucleus Freedom Cochlear Implant System.

Objective: This paper evaluates the benefit of the two-microphone adaptive beamformer BEAM™ in the Nucleus Freedom™ cochlear implant (CI) system for speech understanding in background noise by CI users. Design: A double-blind evaluation of the two-microphone adaptive beamformer BEAM and a hardware directional microphone was carried out with five adult Nucleus CI users. The test procedure consisted of a pre- and post-test in the lab and a 2-wk trial period at home. In the pre- and post-test, the speech reception threshold (SRT) with sentences and the percentage correct phoneme scores for CVC words were measured in quiet and background noise at different signal-to-noise ratios. Performance was assessed for two different noise configurations (with a single noise source and with three noise sources) and two different noise materials (stationary speech-weighted noise and multitalker babble). During the 2-wk trial period at home, the CI users evaluated the noise reduction performance in different listening conditions by means of the SSQ questionnaire. In addition to the perceptual evaluation, the noise reduction performance of the beamformer was measured physically as a function of the direction of the noise source. Results: Significant improvements of both the SRT in noise (average improvement of 5–16 dB) and the percentage correct phoneme scores (average improvement of 10–41%) were observed with BEAM compared to the standard hardware directional microphone. In addition, the SSQ questionnaire and subjective evaluation in controlled and real-life scenarios suggested a possible preference for the beamformer in noisy environments. Conclusions: The evaluation demonstrates that the adaptive noise reduction algorithm BEAM in the Nucleus Freedom CI-system may significantly increase the speech perception by cochlear implantees in noisy listening conditions. This is the first monolateral (adaptive) noise reduction strategy actually implemented in a mainstream commercial CI.

Clinical results of AutoNRT, a completely automatic ECAP recording system for cochlear implants.

Objective: AutoNRT™ is the completely automatic electrically evoked compound action potential (ECAP) measuring algorithm in the recently released Nucleus Freedom cochlear implant system. AutoNRT allows clinicians to automatically record T-NRT profiles that in turn can be used as a guide for initial fitting. The algorithm consists of a pattern recognition part that judges if the traces contain an ECAP and an intelligent flow that optimizes the measurement parameters and finds the ECAP threshold (T-NRT). The objective of this study was to determine how accurate, reliable, and fast the automatic measurements are. Design: Data on more than 400 electrodes were collected as part of the multicenter clinical trial of the Nucleus Freedom cochlear implant system. T-NRT values determined by the algorithm were compared with T-NRT determinations on the same data by different human observers. Also, the time the measurements took was analyzed. Results: In 90% of the cases, the absolute difference between the AutoNRT and the human observer determined T-NRT was less than 9 CL; the median absolute difference was 3 CL. A second experiment, in which a group of human observers were asked to analyze NRT data, showed high variability in T-NRT; in some cases, two experienced clinicians disagreed by more than 30 current levels. Compared with the group, AutoNRT performed as well as the “average” clinician, with the advantage that the AutoNRT threshold determinations are objective. Analysis of the timing data showed an average intraoperative measurement time of less than 20 sec per electrode with a standard deviation of 5 sec, suggesting that the total array of 22 electrodes can be measured intraoperatively in about 7 minutes on average. Conclusions: AutoNRT provides comparable accuracy to an average clinician but with the added benefit of significant time savings over manual recordings. This makes it a valuable tool for clinical measurement of ECAP threshold in cochlear implant recipients.

Low-rank approximation based multichannel wiener filter algorithms for noise reduction with application in cochlear implants

This paper presents low-rank approximation based multichannel Wiener filter algorithms for noise reduction in speech plus noise scenarios, with application in cochlear implants. In a single speech source scenario, the frequency-domain autocorrelation matrix of the speech signal is often assumed to be a rank-1 matrix, which then allows to derive different rank-1 approximation based noise reduction filters. In practice, however, the rank of the autocorrelation matrix of the speech signal is usually greater than one. Firstly, the link between the different rank-1 approximation based noise reduction filters and the original speech distortion weighted multichannel Wiener filter is investigated when the rank of the autocorrelation matrix of the speech signal is indeed greater than one. Secondly, in low input signal-to-noise-ratio scenarios, due to noise non-stationarity, the estimation of the autocorrelation matrix of the speech signal can be problematic and the noise reduction filters can deliver unpredictable noise reduction performance. An eigenvalue decomposition based filter and a generalized eigenvalue decomposition based filter are introduced that include a more robust rank-1, or more generally rank-R, approximation of the autocorrelation matrix of the speech signal. These noise reduction filters are demonstrated to deliver a better noise reduction performance especially in low input signal-to-noise-ratio scenarios. The filters are especially useful in cochlear implants, where more speech distortion and hence a more aggressive noise reduction can be tolerated.

Understanding the effect of noise on electrical stimulation sequences in cochlear implants and its impact on speech intelligibility

The present study investigates the most important factors that limit the intelligibility of the cochlear implant (CI) processed speech in noisy environments. The electrical stimulation sequences provided in CIs are affected by the noise in the following three manners. First of all, the natural gaps in the speech are filled, which distorts the low-frequency ON/OFF modulations of the speech signal. Secondly, speech envelopes are distorted to include modulations of both speech and noise. Lastly, the N-of-M type of speech coding strategies may select the noise dominated channels instead of the dominant speech channels at low signal-to-noise ratios (SNRs). Different stimulation sequences are tested with CI subjects to study how these three noise effects individually limit the intelligibility of the CI processed speech. Tests are also conducted with normal hearing (NH) subjects using vocoded speech to identify any significant differences in the noise reduction requirements and speech distortion limitations between the two subject groups. Results indicate that compared to NH subjects CI subjects can tolerate significantly lower levels of steady state speech shaped noise in the speech gaps but at the same time can tolerate comparable levels of distortions in the speech segments. Furthermore, modulations in the stimulus current level have no effect on speech intelligibility as long as the channel selection remains ideal. Finally, wrong maxima selection together with the introduction of noise in the speech gaps significantly degrades the intelligibility. At low SNRs wrong maxima selection introduces interruptions in the speech and makes it difficult to fuse noisy and interrupted speech signals into a coherent speech stream.

Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users

&NA; Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time‐frequency units, extracts a set of auditory‐inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal‐to‐noise ratio, SNR). This estimate is used to attenuate noise‐dominated and retain speech‐dominated CI channels for electrical stimulation, as in traditional n‐of‐m CI coding strategies. The proposed algorithm was evaluated by measuring the speech‐in‐noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker‐dependent algorithm, that was trained on the target speaker used for testing, and a speaker‐independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker‐dependent algorithm in all noise types and for the speaker‐independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise‐specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices. HighlightsAn algorithm for improving speech understanding in noise for cochlear implant users is evaluated.Significant improvements were found for stationary and non‐stationary noise types.It generalizes to a novel speaker and works over a range of signal‐to‐noise ratios.The small algorithmic delay makes it suitable for real‐time application.

Music mixing preferences of cochlear implant recipients: A pilot study

Abstract Objective: Music perception and appraisal are generally poor in cochlear implant recipients. Simple musical structures, lyrics that are easy to follow, and clear rhythm/beat have been reported among the top factors to enhance music enjoyment. The present study investigated the preference for modified relative instrument levels in music with normal-hearing and cochlear implant subjects. Design: In experiment 1, test subjects were given a mixing console and multi-track recordings to determine their most enjoyable audio mix. In experiment 2, a preference rating experiment based on the preferred relative level settings in experiment 1 was performed. Study sample: Experiment 1 was performed with four postlingually deafened cochlear implant subjects, experiment 2 with ten normal-hearing and ten cochlear implant subjects. Results: A significant difference in preference rating was found between normal-hearing and cochlear implant subjects. The latter preferred an audio mix with larger vocals-to-instruments ratio. In addition, given an audio mix with clear vocals and attenuated instruments, cochlear implant subjects preferred the bass/drum track to be louder than the other instrument tracks. Conclusions: The original audio mix in real-world music might not be suitable for cochlear implant recipients. Modifying the relative instrument level settings potentially improves music enjoyment.

Pitch estimation of a deeply inserted cochlear implant electrode

In this short communication, we evaluate the place-pitch relation of a newly designed, deeply inserted, cochlear implant electrode. The insertion depths ranged from 471° to 662°. Pitch perception was measured in eight subjects with monopolar stimulation on each electrode contact at intensities of 50% and 80% of the dynamic range. We observed a monotonic reduction of pitch estimate with insertion depth. For about half of the subjects, a flattening of the pitch estimate at the basal end of the electrode was seen, while for the other half, pitch continued to decrease monotonically up to the most apical part of the array. We conclude that deeper insertion could increase pitch range for at least some cochlear implant recipients, and could hence potentially increase group performance. Sumario En este breve comunicado evaluamos la relación de lugartono en un electrodo de implante coclear de inserción profunda disen˜ado recientemente. La profundidad de la inserción varía de 471°A 662°. Se midió la percepción del tono en ocho sujetos con estimulación monopolar en cada contacto del electrodo con intensidades del 50% y 80% del rango dinámico. Observamos una reducción monotónica de la estimación del tono con la inserción profunda. Para casi la mitad de los sujetos se observó un aplanamiento de la estimación del tono en el extremo la punta basal del electrodo, mientras que para la otra mitad el tono continuó disminuyendo monotónicamente hasta la parte más apical del hilo de electrodos. Concluimos que la inserción mas profunda podría aumentar el rango tonal en algunos usuarios de implante coclear y por lo tanto también potencialmente mejorar el desempen˜o del grupo.

Rank-1 approximation based multichannel wiener filtering algorithms for noise reduction in cochlear implants

This paper presents multichannel Wiener filtering-based algorithms for noise reduction in cochlear implants. In a single speech scenario, the autocorrelation matrix of the speech signal can be approximated by a rank-1 matrix. It is then possible to derive noise reduction filters that deliver improved signal-to-noise ratio performance. The link between these different filters is investigated here and an eigenvalue decomposition based algorithm is demonstrated to be more stable at low input signal-to-noise ratio compared to previous algorithms.

Binaural noise cue preservation in a binaural noise reduction system with a remote microphone signal

A general binaural noise reduction system is considered that employs the multichannel Wiener filter with partial noise estimation (MWFη) allowing for an explicit tradeoff between noise reduction and binaural noise cue preservation. In this paper, it is assumed that along with the general binaural system, a remote microphone signal with a high input signal-to-noise ratio (SNR) is available for inclusion in the MWFη. The use of this remote microphone signal with a high input SNR allows for a simultaneous increase in both noise reduction performance and preservation of the binaural noise cues. To further increase the performance, a modification to the partial noise estimation (PNE) variable, η, is proposed which relies on exploiting the aforementioned trade-off by either constraining the output SNR or binaural noise cues to the same level before and after the addition of the remote microphone signal. The validity of the theoretical results are supplemented via simulations using a binaural setup with a single speech and noise source.

Speech enhancement based on neural networks applied to cochlear implant coding strategies

Traditionally, algorithms that attempt to significantly improve speech intelligibility in noise for cochlear implant (CI) users have met with limited success, particularly in the presence of a fluctuating masker. In the present study, a speech enhancement algorithm integrating an artificial neural network (NN) into CI coding strategies is proposed. The algorithm decomposes the noisy input signal into time-frequency units, extracts a set of auditory-inspired features and feeds them to the NN to produce an estimation of which CI channels contain more perceptually important information (higher signal-to-noise ratio, SNR). This estimate is then used accordingly to retain a subset of channels for electrical stimulation, as in traditional n-of-m coding strategies. The proposed algorithm was tested with 10 normal-hearing participants listening to CI noise-vocoder simulations against a conventional Wiener filter based enhancement algorithm. Significant improvements in speech intelligibility in stationary and fluctuating noise were found over both unprocessed and Wiener filter processed conditions.