A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications

Window Functions and Their Applications in Signal Processing

Window functions—otherwise known as weighting functions, tapering functions, or apodization functions—are mathematical functions that are zero-valued outside the chosen interval. They are well established as a vital part of digital signal processing. Window Functions and their Applications in Signal Processing presents an exhaustive and detailed account of window functions and their applications in signal processing, focusing on the areas of digital spectral analysis, design of FIR filters, pulse compression radar, and speech signal processing. Comprehensively reviewing previous research and recent developments, this book: Provides suggestions on how to choose a window function for particular applications Discusses Fourier analysis techniques and pitfalls in the computation of the DFT Introduces window functions in the continuous-time and discrete-time domains Considers two implementation strategies of window functions in the time- and frequency domain Explores well-known applications of window functions in the fields of radar, sonar, biomedical signal analysis, audio processing, and synthetic aperture rada

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation

New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Signal concentration and related concepts in time-frequency and on the unit sphere

Unit sphere signal processing is an increasingly active area of research with applications in computer vision, medical imaging, geophysics, cosmology and wireless communications. However, comparing with signal processing in time-frequency domain, characterization and processing of signals defined on the unit sphere is relatively unfamiliar for most of the engineering researchers. In order to better understand and analysis the current issues using the spherical model, such as analysis of brain neural electronic activities in medical imaging and neuroscience, target detection and tracking in radar systems, earthquake occurrence prediction and seismic origin detection in seismology, it is necessary to set up a systematic theory for unit sphere signal processing. How to efficiently analyze and represent functions defined on the unit sphere are central for the unit sphere signal processing, such as filtering, smoothing, detection and estimation in the presence of noise and interference. Slepian-Landau-Pollak time-frequency energy concentration theory and the essential dimensionality of time-frequency signals by the Fourier transform are the fundamental tools for signal processing in the time-frequency domain. Therefore, our research work starts from the analogies of signals between time-frequency and spatial-spectral. In this thesis, we first formulate the k-th moment time-duration weighting measure for a band-limited signal using a general constrained variational method, where a complete, orthonormal set of optimal band-limited functions with the minimum fourth moment time-duration measure is obtained and the prospective applications are discussed. Further, the formulation to an arbitrary signal with second and fourth moment weighting in both time and frequency domain is also developed and the corresponding optimal functions are obtained, which are helpful for practical waveform designs in communication systems. Next, we develop a k-th spatially global moment azimuthal measure (GMZM) and a k-th spatially local moment zenithal measure (LMZM) for real-valued spectral-limited signals. The corresponding sets of optimal functions are solved and compared with the spherical Slepian functions. In addition, a harmonic multiplication operation is developed on the unit sphere. Using this operation, a spectral moment weighting measure to a spatial-limited signal is formulated and the corresponding optimal functions are solved. However, the performance of these sets of functions and their perspective applications in real world, such as efficiently analysis and representation of spherical signals, is still in exploration. Some spherical quadratic functionals by spherical harmonic multiplication operation are formulated in this thesis. Next, a general quadratic variational framework for signal design on the unit sphere is developed. Using this framework and the quadratic functionals, the general concentration problem to an arbitrary signal defined on the unit sphere to simultaneously achieve maximum energy in the finite spatial region and finite spherical spectrum is solved. Finally, a novel spherical convolution by defining a linear operator is proposed, which not only specializes the isotropic convolution, but also has a well defined spherical harmonic characterization. Furthermore, using the harmonic multiplication operation on the unit sphere, a reconstruction strategy without consideration of noise using analysis-synthesis filters under three different sampling methods is discussed