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Simon Grimm examines new multi-microphone signal processing strategies that aim to achieve noise reduction and dereverberation. Therefore, narrow-band signal enhancement approaches are combined with broad-band processing in terms of directivity based beamforming. Previously introduced formulations of the multichannel Wiener filter rely on the second order statistics of the speech and noise signals. The author analyses how additional knowledge about the location of a speaker as well as the microphone arrangement can be used to achieve further noise reduction and dereverberation.



Chapter 1. Introduction

In our daily lives, speech communication devices are omnipresent and have gained much interest in recent years. Widely available consumer products like smartphones, laptops, tablets or the most recently introduced smart loudspeakers are equipped with acoustic sensors that allow to perform a wide range of tasks in terms of speech signal capturing. For example, these include conversations with far end speakers, teleconferencing applications with multiple participants or the use of voice recognition software for speech to text applications, voice control for navigation systems or the utilization of world wide web services.
Simon Grimm

Chapter 2. Noise Reduction using Multichannel Signal Processing Approaches

In the following chapter, basic noise reduction techniques for multichannel microphone setups are derived. Since in this work directivity based signal combining approaches are investigated, the concepts of delay-and-sum beamforming as well as differential beamforming are introduced [9, 16, 17, 18, 19]. These broad-band signal processing techniques allow to create a directional response in terms of an incident angle dependent sensitivity.
Simon Grimm

Chapter 3. The Generalized Multichannel Wiener Filter

In the past few years, research on speech enhancement using acoustic sensor networks consisting of spatially distributed microphones has gained significant interest [28, 29, 30, 31, 7, 32, 6, 33, 34, 35, 36, 37]. Compared with a microphone array at a single position, spatially distributed microphones are able to acquire more information about the sound field. The usage of spatially distributed microphones allows to employ combining techniques for speech quality improvement in reverberant and noisy conditions.
Simon Grimm

Chapter 4. Directivity Based Reference for the Generalized Multichannel Wiener Filter

In this chapter, different references for the G-MWF are presented. In [6], the magnitude of the response \( \tilde{H}_{d} \) was designed to improve the broadband output SNR, whereas the phase term of \( \tilde{H}_{d} \) was set equal to the phase of the ATF in the reference microphone. In [7], an MWF formulation with partial equalization (P-MWF) was introduced, where the overall transfer function was chosen as the envelope of the individual ATFs with the phase of an arbitrary reference microphone. This results in a partial equalization of the acoustic system and an improved broadband output SNR.
Simon Grimm

Chapter 5. Reference for the Binaural Multichannel Wiener Filter

In the last chapter, new reference designs for the multichannel Wiener filter were examined for closely spaced microphones of a monarual hearing aid. This raises the question if these directivity based MWF references can be applied in the context of binaural hearing aids. Research on binaural noise reduction exists for some time [69, 70].
Simon Grimm

Chapter 6. Wind Noise Reduction for a Closely Spaced Microphone Array

In the previous chapters, directivity based references for the generalized multichannel Wiener filter have been discussed. These include the differential beamforming references, which were successfully applied in the context of closely spaced microphone arrangements for hearing aids. However, differential microphone arrays (and therefore the differential references for the G-MWF) are not ideal regarding noise reduction in the presence of wind.
Simon Grimm

Chapter 7. Background Noise Simulation based on MIMO Equalization

In the previous chapters, speech enhancement algorithms were developed that take the spatial information of the sound field into account by using more than one microphone signal. This raises the question how these algorithms can be tested regarding their enhancement capabilities. Additionally, often a comparison with state-of-the-art multiple microphone processing approaches that are already known in the literature is desired (i.e. [104, 105, 106]).
Simon Grimm


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