Abstract
Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome reveals that the signals processed by the blocking-based algorithms are significantly preferred over the noisy signal in terms of instantaneous noise attenuation. Furthermore, the listening test data analysis confirms the conclusions drawn based on the objective evaluation.
Highlights
Hearing loss is a common sensory deficiency, as reported, e.g., in [1]
In [41], we proposed a binaural noise power spectral densities (PSDs) estimator based on the equalization-cancelation technique
This includes the interaural transfer function blockingbased noise PSD estimator (ITFB) [41] (Fig. 2a) and the cross-relation-based noise PSD estimators (CRB) [42] (Fig. 2b), which were previously evaluated under anechoic conditions
Summary
Hearing loss is a common sensory deficiency, as reported, e.g., in [1]. hearing technologies should provide a remarkable compensation of hearing deficits for people with hearing loss. The successful application of the estimated noise power for speech enhancement was initially demonstrated in [41, 42] with hearing aid application In this contribution, a new binaural cue-preserving noise reduction filter, yet based on the MMSE criteria, is proposed (Fig. 1). Based on a common gain function, the mean-square error is rigorously minimized jointly in the left and right ear, thereby delivering optimal noise reduction with exact binaural cue preservation of the target speech and residual noise. To implement the proposed cue-preserving MMSE filter, this paper further investigates and compares a broad range of subspace techniques for noise PSD estimation This includes the interaural transfer function blockingbased noise PSD estimator (ITFB) [41] (Fig. 2a) and the cross-relation-based noise PSD estimators (CRB) [42] (Fig. 2b), which were previously evaluated under anechoic conditions.
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