Source signal representations

For signals represented approximately by the output of a linear system driven by periodic pulse or noise excitations (e.g., human speech or woodwind instruments), the sine-wave model of the previous section can be refined by imposing a source/filter representation on the sine waves components. Within this framework, the notion of phase coherence [Quatieri and McAulay, 1989] is introduced, becoming the basis... 

Nearly all speech analysis is concerned with a three key problems. FirsL we wish to remove the influence of phase second, we wish to perform source/filter separation, so that we can study the spectral envelope of sounds independent of the source that they are spoken with. Finally we often wish to transform these spectral envelopes and source signals into other representations, that are coded more efficiently, have certain robustness properties, or which more clearly show the linguistic information we require. 

The primary reference method used for measuring carbon monoxide in the United States is based on nondispersive infrared (NDIR) photometry (1, 2). The principle involved is the preferential absorption of infrared radiation by carbon monoxide. Figure 14-1 is a schematic representation of an NDIR analyzer. The analyzer has a hot filament source of infrared radiation, a chopper, a sample cell, reference cell, and a detector. The reference cell is filled with a non-infrared-absorbing gas, and the sample cell is continuously flushed with ambient air containing an unknown amount of CO. The detector cell is divided into two compartments by a flexible membrane, with each compartment filled with CO. Movement of the membrane causes a change in electrical capacitance in a control circuit whose signal is processed and fed to a recorder. 

While the mammals predominate in their integration and representation of the sensory world, their noses still tell the brain directly about its chemical space. To explain the workings of accessory olfaction, we need to trace the path of a signal molecule from the moment it leaves its source until a response occurs in the recipient. The events which occur en route will determine the effectiveness of the intended communication. 

Both the CMP and information process in Fig. 3.1 have been simplified in comparison with the representations in Figs. 2.1 and 2.2 because sample preparation has not been considered here and the measurement begins with the measuring sample as information source from which the signals are obtained. 

A mass spectrum is the two-dimensional representation of signal intensity (ordinate) versus m/z (abscissa). The intensity of a peak, as signals are usually called, directly reflects the abundance of ionic species of that respective m/z ratio which have been created from the analyte within the ion source. 

Figure 10.5 Increase of the signal to noise ratio in non-crystallographic symmetry averaging. In (a) is shown a one-dimensional representation of the electron density of a macromolecule. In (b), a graph of the noise that results from the sources of errors in the crystallographic process, including experimental phasing and measurement errors. In (c), the observed density composed of the true electron density with the noise component. In (d), the effect of non-crystallographic symmetry improves the signal from the macromolecule while decreasing the noise level, the dotted lines shows the level of bias.

Griffiths et al. (9) have compared the signal to noise ratios of interferometers and monochrometers under the assumption that the source temperature and resolution for both types of instrument are equivalent. Their analysis involves a comparison of the factors appearing in equation 8 and a representation of the advantage of an interferometer over a monochrometer as the ratio of the factors associated with each instrument. A summary of Griffith et al. s (9) analysis is presented in the balance of this section. 

Ellis et al., 1991] Ellis, D., Vercoe, B.,, and Quatieri, T. (1991). A perceptual representation of audio for co-channel source separation. In Proc. IEEE Workshop Appl. of Signal Processing to Audio and Acoustics, Mohonk Mountain House, New Paltz, NY. 

The purpose of this chapter is to describe the principles of signal analysis/synthesis based on a sine-wave representation and to describe its many speech and music applications. As stated, an important feature of the sinusoidal representation is that the aforementioned sound components can be expressed approximately by a sum of amplitude- and frequency-modulated sine waves. Moreover these sound components, as well as the source and filter contribution to their sine-wave representation, are separable by means of a sine-wave-based decomposition. This separability property is essential in applying sinusoidal analysis/synthesis in a number of areas. 

Fig. 9 Schematic representation of the time-domain DLR (f-DLR) scheme. The shortlived indicator fluorescence and the long-lived phosphorescence of the inert reference beads are simultaneously excited and measured in two time gates. The first (Aex) is in the excitation period where the light source is on and the signal obtained is composed of short-lived fluorescence and long-lived luminescence. The second gate (Aem) is opened in the emission period where the intensity is exclusively composed of the reference luminescence [18]...

Multi-layer feedforward networks contain an input layer connected to one or more layers of hidden neurons (hidden units) and an output layer (Figure 3.5(b)). The hidden units internally transform the data representation to extract higher-order statistics. The input signals are applied to the neurons in the first hidden layer, the output signals of that layer are used as inputs to the next layer, and so on for the rest of the network. The output signals of the neurons in the output layer reflect the overall response of the network to the activation pattern supplied by the source nodes in the input layer. This type of network is especially useful for pattern association (i.e., mapping input vectors to output vectors). 

As described in the previous section, the tracer APO has no significant seasonal sources over the continents, but is mostly generated by the seasonal oceanic O, exchanges. Hence the dilution of this signal into the interiors of the continents in principle provides a test for the transport representation in the models. Of course, the usefulness of this test depends on continental O, monitoring stations, which currently are not existing, but are planned for the near future. 

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