Microphone Array Signal Processing

A "figure-8" sensor is so labeled because its spatial pattern resembles the character "8" with regard to the sensor's axis. This figure-8 pattern narrows as the sensor's order increases. Using two such figure-8 directional sensors of higher order, oriented perpendicularly to each other - this paper pioneers closed-form signal-processing algorithms to estimate an incident signal's azimuth-elevation bivariate direction-of-arrival. Monte Carlo simulations verify these proposed algorithms' efficacy and statistical closeness to the corresponding Cramér-Rao bounds.

We present a prototype of a microphone that moves rapidly along the equator of a rigid spherical scatterer. Our prototype allows for up to 100 rotations per second. It will enable processing methods like beamforming or sound field decomposition that are conventionally performed using microphone arrays. Solutions that assume one or more moving microphones have already been proposed in the literature but have not been verified in practise. Most of these methods require precise knowledge of the instantaneous microphone position, for which no convenient practical solution exists. Recent advancements in microphone array processing enable employing simple microphone trajectories, and recent advancements in 3D printing, microcontrollers, and high-speed electric motors allow for the required control of the movement. This paper presents the design of our prototype and evaluates its performance. Of particular interest is the accuracy of the estimation of the microphone’s instantaneous position. This paper demonstrates that monitoring the passing time instants of a photodiode that is integrated into the rotating sphere provides the highest precision and robustness.

Sound source tracking is a classical problem in the array processing field that finds applications from humanrobot interaction to acoustic navigation. The emergence of deep learning has produced a new class of state-of-the-art solutions, including the Cross3D, based on a convolutional neural network architecture. In this article, the authors present a new system for sound source tracking via microphone array that modifies the pre-processing stage of Cross3D by using MUSIC maps as inputs, and applying appropriate minor modifications to its architecture. The proposed system, called Spectral Cross 3D, improves overall accuracy by 18%, when compared with the original solution.

Sound source tracking is a classical problem in the array processing field that finds applications from humanrobot interaction to acoustic navigation. The emergence of deep learning has produced a new class of state-of-the-art solutions, including the Cross3D, based on a convolutional neural network architecture. In this article, the authors present a new system for sound source tracking via microphone array that modifies the pre-processing stage of Cross3D by using MUSIC maps as inputs, and applying appropriate minor modifications to its architecture. The proposed system, called Spectral Cross 3D, improves overall accuracy by 18%, when compared with the original solution.

In contrast to the adverse environments, performances of existing speech enhancement algorithms do not always produce satisfactory results. In the case of worst signal to noise ratio, the processing is complicated and it may introduce signal distortions and degradation of intelligibility. To overcome the complexity of the existing speech enhancement algorithms, a hybrid concept for enhancing the speech quality and intelligibility is proposed in this research. The primary objectives of the research work is to increase the intelligibility of the speech enhancement system that has been trained for a particular speech signal using modified deep neural network (DNN) and adaptive multi-band spectral subtraction (AdMBSS). In this work, AdMBSS is used for enhancing the intelligibility of the speech signal using the additional phase information calculation, and finally, hybrid DNN and Nelder Mead optimization is utilized to improve the signal quality. Experimental results explain that the proposed framework achieves improved performance in signal to noise ratio, perceptual evaluation of signal quality and minimum mean square error. Finally, performances are taken for the more noises like bus noise, train noise, babble noise, airport noise, station noise and exhibition noise.

In all speech communication settings, the quality and intelligibility of speech is of utmost importance for ease and accuracy of information exchange. The speech processing systems used to communicate or store speech are usually designed for a noise free environment but in a real-world environment, the presence of background interference in the form of additive background and channel noise drastically degrades the performance of these systems, causing inaccurate information exchange and listener fatigue.
The Spectral Subtraction Technique can be used to reduce stationary noise but the non stationary noise still passes through it. Spectral Subtraction also introduces a musical noise which is very annoying to human ears. Beamforming is another possible method of speech enhancement that can be used. Further, the musical noise of Spectral Subtraction can be reduced by Beamforming. Beamforming by itself, however, does not appear to provide enough improvement. Further, the performance of Beamforming becomes worse if the noise source comes from many directions or the speech has strong reverberation.
Therefore, a system has been designed with a combination of Spectral
Subtraction Technique followed by Beamforming Technique reducing stationary as well as residual, musical noise. Algorithms and associated software have been developed for 1) Spectral Subtraction 2)
Beamforming Technique and 3) Spectral Subtraction followed
by Beamforming Technique. The last developed technique results in getting a noise free speech free of musical noise and reverberation making the speech intelligible and of good quality.
Processing of the signal for Spectral Subtraction, Delay Sum Beamforming and the Combined Techniques, was carried out individually for three different experiments (with 3 , 6 and 10 microphones) and for 4 different cases with 3 different signals and
fourth a signal with Gaussian white Noise. The SNR in each case was calculated.