WaveNet-based approximation of a cochlear filtering and hair cell transduction model.

Computational auditory models are important tools for gaining new insights into hearing mechanisms, and they can provide a foundation for bio-inspired speech and audio processing algorithms. However, accurate models often entail an immense computational effort, rendering their application unfeasible if quick execution is required. This paper presents a WaveNet-based approximation of the normal-hearing cochlear filtering and inner hair cell (IHC) transduction stages of a widely used auditory model [Zilany and Bruce (2006).

A subjective evaluation of different music preprocessing approaches in cochlear implant listeners.

Cochlear implants (CIs) can partially restore speech perception to relatively high levels in listeners with moderate to profound hearing loss. However, for most CI listeners, the perception and enjoyment of music remains notably poor. Since a number of technical and physiological restrictions of current implant designs cannot be easily overcome, a number of preprocessing methods for music signals have been proposed recently.

A versatile deep-neural-network-based music preprocessing and remixing scheme for cochlear implant listeners.

While cochlear implants (CIs) have proven to restore speech perception to a remarkable extent, access to music remains difficult for most CI users. In this work, a methodology for the design of deep learning-based signal preprocessing strategies that simplify music signals and emphasize rhythmic information is proposed. It combines harmonic/percussive source separation and deep neural network (DNN) based source separation in a versatile source mixture model.

Interactive Evaluation of a Music Preprocessing Scheme for Cochlear Implants Based on Spectral Complexity Reduction.

Music is difficult to access for the majority of CI users as the reduced dynamic range and poor spectral resolution in cochlear implants (CI), amongst others constraints, severely impair their auditory perception. The reduction of spectral complexity is therefore a promising means to facilitate music enjoyment for CI listeners. We evaluate a spectral complexity reduction method for music signals based on principal component analysis that enforces spectral sparsity, emphasizes the melody contour and attenuates interfering accompanying voices.

Music complexity prediction for cochlear implant listeners based on a feature-based linear regression model.

This paper presents a model for predicting music complexity as perceived by cochlear implant (CI) users. To this end, 10 CI users and 19 normal-hearing (NH) listeners rated 12 selected music pieces on a bipolar music complexity scale and 5 other perception-related scales. The results indicate statistically significant differences in the ratings between CI and NH listeners.

Spectral complexity reduction of music signals based on frequency-domain reduced-rank approximations: An evaluation with cochlear implant listeners.

Methods for spectral complexity reduction of music signals were evaluated in a listening test with cochlear implant (CI) listeners. To this end, reduced-rank approximations were computed in the constant-Q spectral domain using blind and score-informed dimensionality reduction techniques, which were compared to a procedure using a supervised source separation and remixing scheme. Previous works have shown that timbre and pitch cues are transmitted inaccurately through CIs and thus cause perceptual distortions in CI listeners.