An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Enhancement <s> These twenty lectures have been developed and refined by Professor Siebert during the more than two decades he has been teaching introductory Signals and Systems courses at MIT. The lectures are designed to pursue a variety of goals in parallel: to familiarize students with the properties of a fundamental set of analytical tools; to show how these tools can be applied to help understand many important concepts and devices in modern communication and control engineering practice; to explore some of the mathematical issues behind the powers and limitations of these tools; and to begin the development of the vocabulary and grammar, common images and metaphors, of a general language of signal and system theory.Although broadly organized as a series of lectures, many more topics and examples (as well as a large set of unusual problems and laboratory exercises) are included in the book than would be presented orally. Extensive use is made throughout of knowledge acquired in early courses in elementary electrical and electronic circuits and differential equations.Contents: Review of the "classical" formulation and solution of dynamic equations for simple electrical circuits; The unilateral Laplace transform and its applications; System functions; Poles and zeros; Interconnected systems and feedback; The dynamics of feedback systems; Discrete-time signals and linear difference equations; The unilateral Z-transform and its applications; The unit-sample response and discrete-time convolution; Convolutional representations of continuous-time systems; Impulses and the superposition integral; Frequency-domain methods for general LTI systems; Fourier series; Fourier transforms and Fourier's theorem; Sampling in time and frequency; Filters, real and ideal; Duration, rise-time and bandwidth relationships: The uncertainty principle; Bandpass operations and analog communication systems; Fourier transforms in discrete-time systems; Random Signals; Modern communication systems."Circuits, Signals, and Systems" is included in The MIT Press Series in Electrical Engineering and Computer Science, copublished with McGraw-Hill. <s> BIB001 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Enhancement <s> We consider a digital signal processing sensor array system, based on randomly distributed sensor nodes, for surveillance and source localization applications. In most array processing the sensor array geometry is fixed and known and the steering array vector/manifold information is used in beamformation. In this system, array calibration may be impractical due to unknown placement and orientation of the sensors with unknown frequency/spatial responses. This paper proposes a blind beamforming technique, using only the measured sensor data, to form either a sample data or a sample correlation matrix. The maximum power collection criterion is used to obtain array weights from the dominant eigenvector associated with the largest eigenvalue of a matrix eigenvalue problem. Theoretical justification of this approach uses a generalization of Szego's (1958) theory of the asymptotic distribution of eigenvalues of the Toeplitz form. An efficient blind beamforming time delay estimate of the dominant source is proposed. Source localization based on a least squares (LS) method for time delay estimation is also given. Results based on analysis, simulation, and measured acoustical sensor data show the effectiveness of this beamforming technique for signal enhancement and space-time filtering. <s> BIB002 </s> An Overview of the Use of Remote Embedded Sensors for Audio Acquisition and Processing <s> Signal Enhancement <s> In this paper, we derive the maximum-likelihood (ML) location estimator for wideband sources in the near field of the sensor array. The ML estimator is optimized in a single step, as opposed to other estimators that are optimized separately in relative time-delay and source location estimations. For the multisource case, we propose and demonstrate an efficient alternating projection procedure based on sequential iterative search on single-source parameters. The proposed algorithm is shown to yield superior performance over other suboptimal techniques, including the wideband MUSIC and the two-step least-squares methods, and is efficient with respect to the derived Cramer-Rao bound (CRB). From the CRB analysis, we find that better source location estimates can be obtained for high-frequency signals than low-frequency signals. In addition, large range estimation error results when the source signal is unknown, but such unknown parameter does not have much impact on angle estimation. In some applications, the locations of some sensors may be unknown and must be estimated. The proposed method is extended to estimate the range from a source to an unknown sensor location. After a number of source-location frames, the location of the uncalibrated sensor can be determined based on a least-squares unknown sensor location estimator. <s> BIB003
Once the signal is sampled and recorded digitally, many signal enhancement options present themselves. With sufficient local processing capacity, many of these techniques can be applied directly at the sensor, to help with data reduction. In this section we describe several methods of signal enhancement. Filtering. The simplest form of enhancement is filtering to select portions of the signal based on frequency content. There are many different types of filter implementation. The most general method of filtering is to use a Discrete Fourier Transform (DFT) to represent the signal in the frequency domain. In the frequency domain representation, individual frequency ranges can be zeroed out, attenuated, or enhanced arbitrarily. Then, after transforming back to the time domain, the resulting signal will be a filtered version of the original BIB001 . While the DFT is very useful for filtering finite segments of signals, it is more difficult to apply continuously, and is computationally heavier than other filtering algorithms. The Finite Impulse Response (FIR) filter is a second type of filtering algorithm. The FIR produces each output value based on a linear combination of the previous N input values, called "taps." An FIR filter can be designed using a number of different design tools. FIR filters can represent low-pass, high-pass, or band-pass filters, and can provide a variety of "roll-off" characteristics (i.e., how quickly the filter transitions from the pass to stop band). Sharper roll-off requires more taps, hence more state in the filter and more work to compute the linear combination. A concern for filtering systems is the amount of time needed to reach steady state. Until samples are available for all of the N taps, the filter output is ill defined. For systems triggered by analog events (see Section 3.1), this adds to the delay in acquiring the signal. The number of taps required to implement a given FIR filter is inversely proportional to the width of the transition between pass and stop bands, and filter designers should take care to avoid making transition bands narrower than needed. The Exponentially Weighted Moving Average (EWMA) smoothing filter is a third type. An EWMA filter is easy to implement and computationally cheap: at each step, the new output is a weighted sum of the input and the previous output. While EWMA may be a good choice for certain types of application, it suffers from "phase distortion," in which phase delays vary as a function of frequency. Beamforming. When a sensor array is available, beamforming techniques can be used to enhance a signal BIB002 . In beamforming, signals from multiple sensors are combined to produce a single, enhanced version of the signal arriving from a particular direction. Beamforming is based on the principle that signals arriving from a particular direction arrive at the sensors at different times, as a function of the geometry of the array, the direction of arrival, and the propagation speed of the signals. If the direction of arrival (DOA) of a signal is known, the components coming from that direction can be combined together to emphasize the signals from that direction, while de-emphasizing other signals. In the simplest form of this technique, the signals from several microphones are shifted in phase and added together. The phase shift is performed in the frequency domain so that the phase shift need not be an integral number of samples. By adding the shifted signals, signals from the desired direction are emphasized, while other signals will tend be more likely to cancel each other out. Beamforming is often combined with an algorithm to estimate direction of arrival; see for example algorithms based on maximum likelihood estimates BIB003 . These algorithms will typically estimate the direction of arrival for energy found in a specific set of frequencies. The resulting DOA estimate can be used as the input to a beamforming algorithm to enhance the signal, and can be combined across nodes to localize the source.