16 accepted papers
The perception of complex signals such as music or speech in noise is a difficult task for most cochlear implant (CI) users. Furthermore, there is a wide variability in speech recognition so that some also face difficulties in everyday situations with only little or no noise. In this study, two meth…
In this paper, a novel interference mitigation approach using an autoencoder in combination with a traditional interference detection filter is introduced. It is shown that by employing the gated convolution, the encoder has the ability to learn the signal pattern from the remaining interference-fre…
In this paper we present a privacy-aware method for estimating source-dominated microphone clusters in the context of acoustic sensor networks (ASNs). The approach is based on clustered federated learning which we adapt to unsupervised scenarios by employing a light-weight autoencoder model. The mod…
In this paper we propose a new scheme for vehicle engine noise classification as a more privacy-preserving alternative to classifying vehicles based on video recordings. We establish two scenarios: diesel vs. petrol and heavy goods vehicle vs. personal car classification. Our approach includes a nov…
Cochlear implant (CI) users suffer from limitations in music perception and thus prefer music which has a clear rhythm/beat and is played with only a few instruments. Therefore, existing music pre-processing methods aim to enhance music signals for CI users by either emphasizing preferred voices or…
This paper proposes a novel algorithm to estimate the direct-to-reverberant energy ratio (DRR) using hearing aid microphones. The algorithm is based on the interaural magnitude-squared coherence of signals and is able to take both phase and level differences of microphones signals in the binaural co…
This paper introduces a deep neural network based feature extraction scheme that aims to improve the trade-off between utility and privacy in speaker classification tasks. In the proposed scenario we develop a feature representation that helps to maximize the performance of a gender classifier while…
An emphasis on the leading voice or melody is known to facilitate music perception in cochlear implant (CI) listeners while a competing accompaniment is perceived as disturbing. In this paper we present the extension of a monaural music complexity reduction scheme for CI users towards a binaural app…
In this paper we investigate two methods for the preservation of spatial cues in binaural speaker separation. We develop these methods as extensions of our previously proposed model-based generalized sidelobe canceller (GSC) which utilizes a maximum likelihood technique for speaker localization. In…
While speech quality and intelligibility prediction methods for normal-hearing and hearing-impaired listeners have found a lot of attention as a cost-saving complement to listening tests, analogous procedures for music signals are still rare. In this paper a method is proposed for predicting percept…
Since natural acoustic signals like speech or music exhibit a highly varying temporal structure, signal enhancement and feature extraction algorithms benefit from segmentation procedures which take the underlying signal structure into account. In this paper we present a novel unsupervised segmentati…
This paper presents a strategy for using an external microphone for enhancing noisy speech in single-microphone completely-in-canal (CIC) hearing aids. The external microphone is placed such that it benefits from the body shielding noise from the back hemisphere. The presented algorithm first enhanc…
In this paper we present a novel algorithm to localize and separate simultaneous speakers using hearing aids when the head is subject to rotational movement. Most of the algorithms used in hearing aids are able to extract target signals that are in the look direction of the user and suffer from a re…
We propose an approach to compute the inter-subband correlation (ISBC) of noisy speech signals to distinguish between speech and noise segments in the time-frequency plane. The proposed spectral correlation estimator provides information about the input signal which can be used to derive a binary ma…
In this paper we propose a novel approach to cepstral smoothing for reducing musical noise fluctuations in binaural speech enhancement. Similar to other methods, our approach computes a preliminary spectral gain function using the magnitude-squared coherence function and applies an instantaneous wei…
In this paper we propose a novel multi-channel algorithm to separate simultaneous speakers in an environment where the microphone array is subject to movement. When the microphones are mounted to a person's head, for instance, the movements can lead to ambiguities with respect to the sources and to…