Tobias Daniel Rosenkranz

Improving Robustness of Codebook-Based Noise Estimation Approaches With Delta Codebooks

We present a new codebook-based speech enhancement approach which is able to increase robustness of conventional codebook-based approaches against model mismatch and unknown noise types. This is achieved by training only the difference between the actual noise and a robust estimate (e.g., obtained by minimum statistics or recursive minimum tracking) in the cepstral domain instead of the noise itself. The noise codebook is then generated by shifting the so obtained delta-codebook by the cepstral representation of a robust noise estimate. We use the recursive minimum tracking approach as robust estimate. It is thus guaranteed that the robust estimate is also a valid estimate of the codebook-based algorithm. Consequently, the codebook-based algorithm inherits the robustness from the recursive minimum tracking approach. Objective and subjective experiments show that the proposed method yields a consistent quality improvement over the basic codebook-based approach and recursive minimum tracking.

Evaluation of model-based versus non-parametric monaural noise-reduction approaches for hearing aids.

Abstract Objective: Single channel noise reduction has been well investigated and seems to have reached its limits in terms of speech intelligibility improvement, however, the quality of such schemes can still be advanced. This study tests to what extent novel model-based processing schemes might improve performance in particular for non-stationary noise conditions. Design: Two prototype model-based algorithms, a speech-model-based, and a auditory-model-based algorithm were compared to a state-of-the-art non-parametric minimum statistics algorithm. A speech intelligibility test, preference rating, and listening effort scaling were performed. Additionally, three objective quality measures for the signal, background, and overall distortions were applied. For a better comparison of all algorithms, particular attention was given to the usage of the similar Wiener-based gain rule. Study sample: The perceptual investigation was performed with fourteen hearing-impaired subjects. Results: The results revealed that the non-parametric algorithm and the auditory model-based algorithm did not affect speech intelligibility, whereas the speech-model-based algorithm slightly decreased intelligibility. In terms of subjective quality, both model-based algorithms perform better than the unprocessed condition and the reference in particular for highly non-stationary noise environments. Conclusion: Data support the hypothesis that model-based algorithms are promising for improving performance in non-stationary noise conditions.

Modeling the temporal evolution of LPC parameters for codebook-based speech enhancement

Conventional statistical single-channel noise reduction methods suffer from bad performance in highly non-stationary environments. In contrast to that, model-based algorithms have the potential to deal with those adverse conditions. In this paper, we focus on codebook-based algorithms which utilize trained codebooks where typical speech and noise spectral shapes are stored. Speech and noise estimates are determined frame for frame independently which allows to deal with highly non-stationary noise. By incorporating memory, the performance can be further improved. In this paper, elaborated models for memory modeling are presented and a preliminary validation is provided.