Low Delay Noise Reduction and Dereverberation for Hearing Aids

Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009, Article ID 437807, 9 pages doi:10.1155/2009/437807 Research Article Low Delay Noise Reduction and Dereverberation for Hearing Aids Heinrich W. Löllmann (EURASIP Member) and Peter Vary Institute of Communication Systems and Data Processing, RWTH Aachen University, 52056 Aachen, Germany Correspondence should be addressed to Heinrich W. Löllmann, Received 11 December 2008; Accepted 16 March 2009 Recommended by Heinz G. Goeckler A new system for single-channel speech enhancement is proposed which achieves a joint suppression of late reverberant speech and background noise with a low signal delay and low computational complexity. It is based on a generalized spectral subtraction rule which depends on the variances of the late reverberant speech and background noise. The calculation of the spectral variances of the late reverberant speech requires an estimate of the reverberation time (RT) which is accomplished by a maximum likelihood (ML) approach. The enhancement with this blind RT estimation achieves almost the same speech quality as by using the actual RT. In comparison to commonly used post-filters in hearing aids which only perform a noise reduction, a significantly better objective and subjective speech quality is achieved. The proposed system performs time-domain filtering with coefficients adapted in the non-uniform (Bark-scaled) frequency-domain. This allows to achieve a high speech quality with low signal delay which is important for speech enhancement in hearing aids or related applications such as hands-free communication systems. Copyright © 2009 H. W. Löllmann and P. Vary. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction Algorithms for the enhancement of acoustically disturbed speech signals have been the subject of intensive research over the last decades, cf., [1–3]. The wide-spread use of mobile communication devices and, not at least, the introduction of digital hearing aids have contributed significantly to the interest in this field. For hearing impaired people, it is especially difficult to communicate with other persons in noisy environments. Therefore, speech enhancement systems have become an integral component of modern hearing aids. However, despite significant progress, the development of speech enhancement systems for hearing aids is still a very challenging problem due to the demanding requirements regarding computational complexity, signal delay and speech quality. A common approach is to use a beamformer with two or three closely spaced microphones followed by a post-filter, e.g., [4, 5]. An adaptive beamformer is often used, implemented by first- or second- order differential microphone arrays or a generalized sidelobe canceller (GSC), respectively, e.g., [5]. Due to the use of small microphone arrays, only a limited noise suppression can be achieved by this, especially for diffuse noise fields. Therefore, the output signal of the beamformer is further processed by a (Wiener) post-filter to achieve an improved noise suppression, e.g., [4–7]. A related approach is to use an extension of the GSC structure termed as speech distortion weighted multichannel Wiener filter [8, 9]. This approach allows to balance the tradeoff between speech distortions and noise reduction and is more robust towards reverberation than a common GSC. So far, such systems achieve only a very limited suppression of speech distortions due to room reverberation. Such impairments are caused by the multiple reflections and diffraction of the sound on walls and objects of a room. These multiple echoes add to the direct sound at the receiver and blur its temporal and spectral characteristics. As a consequence, reverberation and background noise reduce listening comfort and speech intelligibility, especially for hearing impaired persons [10, 11]. Therefore, algorithms for a joint suppression of background noise and reverberation effects are of special interest for speech enhancement in hearing instruments. However, many proposals are less suitable for this application. For example, dereverberation algorithms based on linear prediction such as [12] achieve mainly a reduction of early reflections and do not consider additive noise, 2 EURASIP Journal on Advances in Signal Processing while algorithms based on a time-averaging [13] exhibit a high signal delay. Coherence-based speech enhancement algorithms such as [14] or [15] can suppress background noise and reverberation, but they are rather ineffective if only two closely spaced microphones can be used. This problem can be alleviated to some extend by a noise classification and binaural processing [16] which, however, requires two hearing aid devices connected by a wireless data link. A single-channel algorithm for speech dereverberation and noise reduction has been proposed recently in [17]. However, this algorithm is less suitable for hearing aids due to its high computational complexity and signal delay as well as its strong speech distortions. A more powerful approach for noise reduction and dereverberation is to use blind source separation (BSS), e.g., [18]. Such algorithms do not require a priori knowledge about the microphone positions or source locations. However, they depend on a full data link between the hearing aid devices and possess a high computational complexity. Therefore, further work remains to be done to integrate such algorithms into common hearing instruments [19]. In this contribution, a single-channel speech enhancement algorithm is proposed, which is more suitable for current hearing aid devices. It performs a suppression of background noise and late reverberant speech by means of a generalized spectral subtraction. The devised (post-)filter exhibits a low signal delay, which is important in hearing aids, e.g., to avoid comb filter effects. The calculation of the late reverberant speech energy requires (only) an estimate of the reverberation time (RT), which is accomplished by a maximum likelihood (ML) approach. Thus, no explicit speech modeling is involved in the dereverberation process as, e.g., in [20] such that an estimation of speech model parameters is not needed here. The paper is organized as follows. In Section 2, the underlying signal model is introduced. The overall system for low delay speech enhancement is outlined in Section 3. The calculation of the spectral weights for noise reduction and dereverberation is treated in Section 4. An important issue is the determination of the spectral variances of the late reverberant speech, which in turn is based on an estimation of the RT. These issues are treated in Sections 4.2 and 4.3. The performance of the new system is analyzed in Section 5, and the main results are summarized i (...truncated)