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Cochlear implant (CI) recipients report severe degradation of speech understanding under noisy conditions. Most CI recipients typically can require about 10–25 dB higher signal-to-noise ratio than normal hearing (NH) listeners in order to achieve similar speech understanding performance. In recent years, significant emphasis has been put on binaural algorithms, which not only make use of the head shadow effect, but also have two or more microphone signals at their disposal to generate binaural inputs. Most of the CI recipients today are unilaterally implanted but they can still benefit from the binaural processing utilizing a contralateral microphone. The phase error filtering (PEF) algorithm tries to minimize the phase error variance utilizing a time–frequency mask for noise reduction. Potential improvement in speech intelligibility offered by the algorithm is evaluated with four different kinds of mask functions. The study reveals that the PEF algorithm which uses a contralateral microphone but unilateral presentation provides considerable improvement in intelligibility for both NH and CI subjects. Further, preference rating test suggests that CI subjects can tolerate higher levels of distortions than NH subjects, and therefore, more aggressive noise reduction for CI recipients is possible. [ABSTRACT FROM PUBLISHER]

Over the last decade, the detection of auditory steady-state responses (ASSR) has been developed for reliable hearing threshold estimation at audiometric frequencies. Unfortunately, the duration of ASSR measurement can be long, which is unpractical for wide scale clinical application. In this paper, we propose independent component analysis (ICA) as a tool to improve the ASSR detection in recorded single-channel as well as multichannel electroencephalogram (EEG) data. We conclude that ICA is able to reduce measurement duration significantly. For a multichannel implementation, near-optimal performance is obtained with five-channel recordings. [ABSTRACT FROM AUTHOR]

In this paper, the first real-time implementation and perceptual evaluation of a singular value decomposition (SVD)-based optimal filtering technique for noise reduction in a dual microphone behind-the-ear (BTE) hearing aid is presented. This evaluation was carried out for a speech weighted noise and multitalker babble, for single and multiple jammer sound source scenarios. Two basic microphone configurations in the hearing aid were used. The SVD-based optimal filtering technique was compared against an adaptive beamformer, which is known to give significant improvements in speech intelligibility in noisy environment. The optimal filtering technique works without assumptions about a speaker position, unlike the two-stage adaptive beamformer. However this strategy needs a robust voice activity detector (VAD). A method to improve the performance of the VAD was presented and evaluated physically. By connecting the VAD to the output of the noise reduction algorithms, a good discrimination between the speech-and-noise periods and the noise-only periods of the signals was obtained. The perceptual experiments demonstrated that the SVD-based optimal filtering technique could perform as well as the adaptive beamformer in a single noise source scenario, i.e., the ideal scenario for the latter technique, and could outperform the adaptive beamfonner in multiple noise source scenarios. [ABSTRACT FROM AUTHOR]

Objective: Listening to music is still one of the more challenging aspects of using a cochlear implant (CI) for most users. Simple musical structures, a clear rhythm/beat, and lyrics that are easy to follow are among the top factors contributing to music appreciation for CI users. Modifying the audio mix of complex music potentially improves music enjoyment in CI users. Methods: A stereo music preprocessing scheme is described in which vocals, drums, and bass are emphasized based on the representation of the harmonic and the percussive components in the input spectrogram, combined with the spatial allocation of instruments in typical stereo recordings. The scheme is assessed with postlingually deafened CI subjects (N = 7) using pop/rock music excerpts with different complexity levels. Results: The scheme is capable of modifying relative instrument level settings, with the aim of improving music appreciation in CI users, and allows individual preference adjustments. The assessment with CI subjects confirms the preference for more emphasis on vocals, drums, and bass as offered by the preprocessing scheme, especially for songs with higher complexity. Conclusion: The stereo music preprocessing scheme has the potential to improve music enjoyment in CI users by modifying the audio mix in widespread (stereo) music recordings. Significance: Since music enjoyment in CI users is generally poor, this scheme can assist the music listening experience of CI users as a training or rehabilitation tool. [ABSTRACT FROM PUBLISHER]

Auditory steady-state responses (ASSRs) in EEG measurements are currently used for reliable hearing threshold estimation at audiometric frequencies. Especially, newborns with hearing problems benefit from this technique, as with this information, diagnosis can be better specified and hearing aids can be better fitted at an early age. Unfortunately, measurement duration is still very long for clinical widespread use due to the lack of efficient signal detection techniques with sufficient robustness against artifacts. In this paper, a simplified procedural framework for ASSR detection is worked out that allows the development of a multichannel processing strategy, starting from a detection theory approach. It is shown that a sufficient statistic can be calculated that best captures the amount of ASSR in the recorded data. The evaluation is conducted using data from ten normal-hearing adults. It is concluded that most single- and multichannel approaches are similar in performance when applied to uncontaminated EEG. When artifact-rich EEG is used, the proposed detection-theory-based approach significantly improves the number of ASSR detections compared with a noise-weighted common EEG channel derivation (vertex-occiput). [ABSTRACT FROM AUTHOR]