1.
Loudspeaker
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A loudspeaker is an electroacoustic transducer, which converts an electrical audio signal into a corresponding sound. The most widely used type of speaker in the 2010s is the speaker, invented in 1925 by Edward W. Kellogg. The dynamic speaker operates on the basic principle as a dynamic microphone. Besides this most common method, there are several technologies that can be used to convert an electrical signal into sound. The sound source must be amplified or strengthened with a power amplifier before the signal is sent to the speaker. Speakers are typically housed in an enclosure or speaker cabinet which is often a rectangular or square box made of wood or sometimes plastic. The enclosures materials and design play an important role in the quality of the sound, where high fidelity reproduction of sound is required, multiple loudspeaker transducers are often mounted in the same enclosure, each reproducing a part of the audible frequency range. In this case the individual speakers are referred to as drivers, drivers made for reproducing high audio frequencies are called tweeters, those for middle frequencies are called mid-range drivers, and those for low frequencies are called woofers. Smaller loudspeakers are found in such as radios, televisions, portable audio players, computers. Larger loudspeaker systems are used for music, sound reinforcement in theatres and concerts, the term loudspeaker may refer to individual transducers or to complete speaker systems consisting of an enclosure including one or more drivers. To adequately reproduce a range of frequencies with even coverage, most loudspeaker systems employ more than one driver. Individual drivers are used to different frequency ranges. The drivers are named subwoofers, woofers, mid-range speakers, tweeters, the terms for different speaker drivers differ, depending on the application. In two-way systems there is no mid-range driver, so the task of reproducing the mid-range sounds falls upon the woofer and tweeter, home stereos use the designation tweeter for the high frequency driver, while professional concert systems may designate them as HF or highs. When multiple drivers are used in a system, a network, called a crossover. Loudspeaker driver of the type pictured are termed dynamic to distinguish them from earlier drivers, or speakers using piezoelectric or electrostatic systems, or any of several other sorts. Johann Philipp Reis installed an electric loudspeaker in his telephone in 1861, it was capable of reproducing clear tones, alexander Graham Bell patented his first electric loudspeaker as part of his telephone in 1876, which was followed in 1877 by an improved version from Ernst Siemens. In 1898, Horace Short patented a design for a loudspeaker driven by compressed air, he sold the rights to Charles Parsons

2.
Spectrum
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A spectrum is a condition that is not limited to a specific set of values but can vary, without steps, across a continuum. The word was first used scientifically in optics to describe the rainbow of colors in visible light after passing through a prism, as scientific understanding of light advanced, it came to apply to the entire electromagnetic spectrum. Spectrum has since been applied by analogy to topics outside of optics, thus, one might talk about the spectrum of political opinion, or the spectrum of activity of a drug, or the autism spectrum. In these uses, values within a spectrum may not be associated with precisely quantifiable numbers or definitions, such uses imply a broad range of conditions or behaviors grouped together and studied under a single title for ease of discussion. Nonscientific uses of the spectrum are sometimes misleading. For instance, a single left–right spectrum of opinion does not capture the full range of peoples political beliefs. Political scientists use a variety of biaxial and multiaxial systems to accurately characterize political opinion. In most modern usages of spectrum there is a theme between the extremes at either end. This was not always true in older usage, in Latin spectrum means image or apparition, including the meaning spectre. Spectral evidence is testimony about what was done by spectres of persons not present physically and it was used to convict a number of persons of witchcraft at Salem, Massachusetts in the late 17th century. The word spectrum was used to designate a ghostly optical afterimage by Goethe in his Theory of Colors and Schopenhauer in On Vision. The prefix spectro- is used to form words relating to spectra, for example, a spectrometer is a device used to record spectra and spectroscopy is the use of a spectrometer for chemical analysis. In the 17th century the word spectrum was introduced into optics by Isaac Newton, soon the term referred to a plot of light intensity or power as a function of frequency or wavelength, also known as a spectral density plot. The term spectrum was expanded to apply to other waves, such as sound waves that could also be measured as a function of frequency, frequency spectrum and power spectrum of a signal. The term now applies to any signal that can be measured or decomposed along a variable such as energy in electron spectroscopy or mass to charge ratio in mass spectrometry. Spectrum is also used to refer to a representation of the signal as a function of the dependent variable. Devices used to measure an electromagnetic spectrum are called spectrograph or spectrometer, the visible spectrum is the part of the electromagnetic spectrum that can be seen by the human eye. The wavelength of light ranges from 390 to 700 nm

3.
Waterfall chart
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A waterfall chart is a form of data visualization that helps in understanding the cumulative effect of sequentially introduced positive or negative values. The waterfall chart is also known as a flying bricks chart or Mario chart due to the apparent suspension of columns in mid-air, often in finance, it will be referred to as a bridge. Waterfall charts were popularized by the consulting firm McKinsey & Company in its presentations to clients. The waterfall or Bridge chart is used for understanding how an initial value is affected by a series of intermediate positive or negative values. Usually the initial and the values are represented by whole columns. The columns are color-coded for distinguishing between positive and negative values, Waterfall charts can be used for various types of quantitative analysis, ranging from inventory analysis to performance analysis. There are several sources for automatic creations of Waterfall Charts

4.
Spectrogram
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A spectrogram is a visual representation of the spectrum of frequencies in a sound or other signal as they vary with time or some other variable. Spectrograms are sometimes called spectral waterfalls, voiceprints, or voicegrams, spectrograms can be used to identify spoken words phonetically, and to analyse the various calls of animals. They are used extensively in the development of the fields of music, sonar, radar, the frequency and amplitude axes can be either linear or logarithmic, depending on what the graph is being used for. Audio would usually be represented with a logarithmic amplitude axis, and frequency would be linear to emphasize harmonic relationships, or logarithmic to emphasize musical, tonal relationships. Spectrograms are usually created in one of two ways, approximated as a filterbank that results from a series of filters, or calculated from the time signal using the Fourier transform. These two methods actually form two different time–frequency representations, but are equivalent under some conditions, creating a spectrogram using the FFT is a digital process. Digitally sampled data, in the domain, is broken up into chunks, which usually overlap. Each chunk then corresponds to a line in the image. These spectrums or time plots are laid side by side to form the image or a three-dimensional surface, or slightly overlapped in various ways. Early analog spectrograms were applied to a range of areas including the study of bird calls, with current research continuing using modern digital equipment. Contemporary use of the spectrogram is especially useful for studying frequency modulation in animal calls. Specifically, the characteristics of FM chirps, broadband clicks. By reversing the process of producing a spectrogram, it is possible to create a signal whose spectrogram is an arbitrary image and this technique can be used to hide a picture in a piece of audio and has been employed by several electronic music artists. See Audio timescale-pitch modification and Phase vocoder, spectrograms can be used to analyze the results of passing a test signal through a signal processor such as a filter in order to check its performance. The Analysis & Resynthesis Sound Spectrograph is an example of a program that attempts to do this. The Pattern Playback was a speech synthesizer, designed at Haskins Laboratories in the late 1940s. In fact, there is some information in the spectrogram. The size and shape of the window can be varied

5.
Spectral density estimation
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In statistical signal processing, the goal of spectral density estimation is to estimate the spectral density of a random signal from a sequence of time samples of the signal. Intuitively speaking, the spectral density characterizes the frequency content of the signal, one purpose of estimating the spectral density is to detect any periodicities in the data, by observing peaks at the frequencies corresponding to these periodicities. Some SDE techniques assume that a signal is composed of a number of generating frequencies plus noise and seek to find the location. Others make no assumption on the number of components and seek to estimate the whole generating spectrum, Spectrum analysis, also referred to as frequency domain analysis or spectral density estimation, is the technical process of decomposing a complex signal into simpler parts. As described above, many processes are best described as a sum of many individual frequency components. Any process that quantifies the various amounts, versus frequency can be called spectrum analysis, Spectrum analysis can be performed on the entire signal. Alternatively, a signal can be broken into segments. Periodic functions are particularly well-suited for this sub-division, general mathematical techniques for analyzing non-periodic functions fall into the category of Fourier analysis. The Fourier transform of a function produces a spectrum which contains all of the information about the original signal. This means that the function can be completely reconstructed by an inverse Fourier transform. For perfect reconstruction, the analyzer must preserve both the amplitude and phase of each frequency component. These two pieces of information can be represented as a 2-dimensional vector, as a number, or as magnitude. A common technique in signal processing is to consider the squared amplitude, or power, because of reversibility, the Fourier transform is called a representation of the function, in terms of frequency instead of time, thus, it is a frequency domain representation. Linear operations that could be performed in the domain have counterparts that can often be performed more easily in the frequency domain. Frequency analysis also simplifies the understanding and interpretation of the effects of various time-domain operations, for instance, only non-linear or time-variant operations can create new frequencies in the frequency spectrum. The DFT is almost invariably implemented by an efficient algorithm called fast Fourier transform, but the periodogram does not provide processing-gain when applied to noiselike signals or even sinusoids at low signal-to-noise ratios. In other words, the variance of its estimate at a given frequency does not decrease as the number of samples used in the computation increases. This can be mitigated by averaging time or over frequency

6.
Impulse response
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In signal processing, the impulse response, or impulse response function, of a dynamic system is its output when presented with a brief input signal, called an impulse. More generally, a response is the reaction of any dynamic system in response to some external change. In both cases, the impulse response describes the reaction of the system as a function of time, in all these cases, the dynamic system and its impulse response may be actual physical objects, or may be mathematical systems of equations describing such objects. Since the impulse function contains all frequencies, the impulse response defines the response of a linear time-invariant system for all frequencies, mathematically, how the impulse is described depends on whether the system is modeled in discrete or continuous time. The impulse can be modeled as a Dirac delta function for continuous-time systems, the Dirac delta represents the limiting case of a pulse made very short in time while maintaining its area or integral. While this is impossible in any system, it is a useful idealisation. In Fourier analysis theory, such an impulse comprises equal portions of all possible excitation frequencies, any system in a large class known as linear, time-invariant is completely characterized by its impulse response. That is, for any input, the output can be calculated in terms of the input, the impulse response of a linear transformation is the image of Diracs delta function under the transformation, analogous to the fundamental solution of a partial differential operator. It is usually easier to analyze systems using transfer functions as opposed to impulse responses, the transfer function is the Laplace transform of the impulse response. The Laplace transform of a systems output may be determined by the multiplication of the function with the inputs Laplace transform in the complex plane. An inverse Laplace transform of this result will yield the output in the time domain, to determine an output directly in the time domain requires the convolution of the input with the impulse response. When the transfer function and the Laplace transform of the input are known, the impulse response, considered as a Greens function, can be thought of as an influence function, how a point of input influences output. In practical systems, it is not possible to produce an impulse to serve as input for testing, therefore. Provided that the pulse is short compared to the impulse response. An application that demonstrates this idea was the development of impulse response loudspeaker testing in the 1970s, loudspeakers suffer from phase inaccuracy, a defect unlike other measured properties such as frequency response. Impulse response analysis is a facet of radar, ultrasound imaging. An interesting example would be broadband internet connections, dSL/Broadband services use adaptive equalisation techniques to help compensate for signal distortion and interference introduced by the copper phone lines used to deliver the service. In control theory the impulse response is the response of a system to a Dirac delta input, in acoustic and audio applications, impulse responses enable the acoustic characteristics of a location, such as a concert hall, to be captured