Efficient Continuous Beam Steering for Planar Arrays of Differential Microphones

Performing continuous beam steering, from planar arrays of high-order differential microphones, is not trivial. The main problem is that shape-preserving beams can be steered only in a finite set of privileged directions, which depend on the position and the number of physical microphones. In this letter, we propose a simple and computationally inexpensive method for alleviating this problem using planar microphone arrays. Given two identical reference beams pointing in two different directions, we show how to build a beam of nearly constant shape, which can be continuously steered between such two directions. The proposed method, unlike the diffused steering approaches based on linear combinations of eigenbeams (spherical harmonics), is applicable to planar arrays also if we deal with beams characterized by high-order polar patterns. Using the coefficients of the Fourier series of the polar patterns, we also show how to find a tradeoff between shape invariance of the steered beam, and maximum angular displacement between the two reference beams. We show the effectiveness of the proposed method through the analysis of models based on first-, second-, and third-order differential microphones

Speech Enhancement using Fiber Acoustic Sensor

With the development of IoT (Internet of Things) services and devices, the voice command becomes a more and more important tool for human computer interaction. However, the audio signal recorded by the conventional omni-directional microphone is easy to be corrupted by the environmental noise like interference speech. Although the conventional beamforming techniques are able to point the main lobe of beam pattern at the desired speaker, it requires several omni microphones to form a microphone array, which will occupy large space on an IoT device. Many researchers are devoting their efforts to inventing a microphone of small size that can create directional beam pattern. Recently, researchers get inspirations from the spider’s way to sense the acoustic wave. They invented a new small-size acoustic sensor made of spider silks. This acoustic sensor has a frequency-independent dipole beam pattern for wideband audio signal. Utilizing this fiber acoustic sensor, two compact microphone arrays and corresponding speech enhancement systems can be constructed. The first microphone array consists of one omni-microphone collocated with one fiber acoustic sensor. And the second one consists of two collocated fiber acoustic sensors with orthogonal dipole beam patterns. By using the first microphone array, a first-order adaptive beamformer is designed in this thesis to reduce speech interference effects and separate speeches. In this design, an adaptive first-order beam pattern is formed by means of normalized least mean square method. Considering a scenario where the desired speech and interference speech are present at the same time, this adaptive beamformer is able to point the null angle of beam pattern at the undesired speaker to achieve speech interference reduction. In order to verify this idea, numerical simulations are conducted in an ideal condition (clean speech without reverberation) and real scenario (clean speech corrupted by white noise and reverberation). The results show that this design is able to improve speech quality significantly in ideal case. Under the condition suffering from white noise and reverberation, the improvement is achieved as well but at a much smaller scale. By using the second collocated microphone array, a speech enhancement system is proposed to make the collocated fiber acoustic sensors be able to capture speech from any directions. This system includes three main parts. The first part conducts DOA (direction of arrival) estimation empowered by a machine learning method. Here the inter-channel acoustic intensity difference is employed to compute raw DOA estimates with the presence of white noise and reverberation. After obtaining the raw DOA estimates, the machine learning method (wrapped Gaussian mixture model) is used to give a more accurate DOA estimation. This proposed method is robust to both white noise and reverberation with a low computational complexity and solves the phase ambiguity problem (0 and π are identical). In the second part, by using the orthogonality of the dipoles of the two collocated fiber acoustic sensors (one is sin⁡θ and the other is cos⁡θ), along with the DOA (θ) estimated by the wrapped Gaussian mixture model, a steerable dipole beam pattern is generated to point the main lobe at the speaker. In the third part, a noise reduction procedure is applied to the output signal of the steerable beamformer. The proposed method is based on a time-frequency mask, which is used to filter out time-frequency bins of white noise and keep those of speech signal. In order to verify the effectiveness of the designed system, numerical simulations are conducted in the existence of both white noise and reverberation. The result shows that the proposed DOA estimation method is robust to both white noise and reverberation. It implies that this type of microphone array is able to obtain precise speaker spatial information. Meanwhile, the audio quality of the output signal of this system is improved by at least 50%

Array signal processing for source localization and enhancement

“A common approach to the wide-band microphone array problem is to assume a certain array geometry and then design optimal weights (often in subbands) to meet a set of desired criteria. In addition to weights, we consider the geometry of the microphone arrangement to be part of the optimization problem. Our approach is to use particle swarm optimization (PSO) to search for the optimal geometry while using an optimal weight design to design the weights for each particle’s geometry. The resulting directivity indices (DI’s) and white noise SNR gains (WNG’s) form the basis of the PSO’s fitness function. Another important consideration in the optimal weight design are several regularization parameters. By including those parameters in the particles, we optimize their values as well in the operation of the PSO. The proposed method allows the user great flexibility in specifying desired DI’s and WNG’s over frequency by virtue of the PSO fitness function. Although the above method discusses beam and nulls steering for fixed locations, in real time scenarios, it requires us to estimate the source positions to steer the beam position adaptively. We also investigate source localization of sound and RF sources using machine learning techniques. As for the RF source localization, we consider radio frequency identification (RFID) antenna tags. Using a planar RFID antenna array with beam steering capability and using received signal strength indicator (RSSI) value captured for each beam position, the position of each RFID antenna tag is estimated. The proposed approach is also shown to perform well under various challenging scenarios”--Abstract, page iv

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

A Study into Speech Enhancement Techniques in Adverse Environment

This dissertation developed speech enhancement techniques that improve the speech quality in applications such as mobile communications, teleconferencing and smart loudspeakers. For these applications it is necessary to suppress noise and reverberation. Thus the contribution in this dissertation is twofold: single channel speech enhancement system which exploits the temporal and spectral diversity of the received microphone signal for noise suppression and multi-channel speech enhancement method with the ability to employ spatial diversity to reduce reverberation

MICROPHONE ARRAY OPTIMIZATION IN IMMERSIVE ENVIRONMENTS

The complex relationship between array gain patterns and microphone distributions limits the application of traditional optimization algorithms on irregular arrays, which show enhanced beamforming performance for human speech capture in immersive environments. This work analyzes the relationship between irregular microphone geometries and spatial filtering performance with statistical methods. Novel geometry descriptors are developed to capture the properties of irregular microphone distributions showing their impact on array performance. General guidelines and optimization methods for regular and irregular array design are proposed in immersive (near-field) environments to obtain superior beamforming ability for speech applications. Optimization times are greatly reduced through the objective function rules using performance-based geometric descriptions of microphone distributions that circumvent direct array gain computations over the space of interest. In addition, probabilistic descriptions of acoustic scenes are introduced to incorporate various levels of prior knowledge for the source distribution. To verify the effectiveness of the proposed optimization methods, simulated gain patterns and real SNR results of the optimized arrays are compared to corresponding traditional regular arrays and arrays obtained from direct exhaustive searching methods. Results show large SNR enhancements for the optimized arrays over arbitrary randomly generated arrays and regular arrays, especially at low microphone densities. The rapid convergence and acceptable processing times observed during the experiments establish the feasibility of proposed optimization methods for array geometry design in immersive environments where rapid deployment is required with limited knowledge of the acoustic scene, such as in mobile platforms and audio surveillance applications

advances in wave digital modeling of linear and nonlinear systems a summary

This brief summarizes some of the main research results that I obtained during the three years, ranging from November 2015 to October 2018, as a Ph.D. student at Politecnico di Milano under the supervision of Professor Augusto Sarti, and that are contained in my doctoral dissertation, entitled "Advances in Wave Digital Modeling of Linear and Nonlinear Systems". The thesis provides contributions to all the main aspects of Wave Digital (WD) modeling of lumped systems: it introduces generalized definitions of wave variables; it presents novel WD models of one- and multi-port linear and nonlinear circuit elements; it discusses systematic techniques for the WD implementation of arbitrary connection networks and it describes a novel iterative method for the implementation of circuits with multiple nonlinear elements. Though WD methods usually focus on the discrete-time implementation of analog audio circuits; the methodologies addressed in the thesis are general enough as to be applicable to whatever system that can be described by an equivalent electric circuit