Anne Briggs _-_ A collection
This is my favourite Anne Briggs Album. It really shows her whole repotoire.
enjoy
http://rapidshare.de/files/15164605/Anne_Briggs_A_collection.rar.html
password for all files here @ packet switcher are [ thepacketswitcher]
posted by the packet switcher at 4:34 PM
3 Comments:
What is the password?
Thank you for this one! I don't know her, but you made me curious, so I'll try.
The other record of her you placed here was only 27 MB, so I guess 128kbps at the most.
That's not good enough for me to bring emotions across, sorry.
Music has the ability to ensnare us all, not only with its mellifluous tones and rhythmic beats, but with its academic intrigue as to why and how it has such power. How does music affect us? To enjoy a song is to crawl inside its layered patterns and let the sound take over your mind; it is a process of seduction. An upset man can be seduced to tranquility, a lucid man can lose his sanity; religious or political views can be glorified and sadness can seem sexy. Music can be used for mating, communication, artistic expression, motivation, and the apotheosis of essentially anything. Throughout these behavioral affects, there is an underlying neurological process responding to the aforementioned type of aurally perceived pattern. This has been the source of many debates, essentially among evolutionary psychologists and neurologists who at first were baffled as to why a series of patterns of resonant frequencies can have so much power. Why are we so attracted to resonance? There are millions of different sounds we could have found beautiful, but resonance is what all musical creatures have agreed upon. Whales, birds, and humans all have decided on resonance and the resulting patterns of resonance that we are all familiar with (Gray, 2001).
Music itself can be broken down into three main components: rhythm, melody, and lyrics. All three of these contribute to a song, although some genre’s concentrate on one aspect or another. Techno tends to concentrate on the beat; vocal and new-age tend to focus on the melody, while hip hop and folk music concentrate on the poetry. Rhythm is found everywhere in life; the brain is not excluded from this generalization.
Hearing, or audition, is one of the traditional five senses, and refers to the ability to detect sound.
In human beings, hearing is performed by the ears, which also perform the function of balance, a sense in itself but not one of the traditional list (due to Aristotle). This is in common with most mammals. Many other organisms also have some form of hearing, either by some sort of ear, or by other structures, or by a combination.
A common rule of thumb used to describe human hearing is that human hearing is sensitive in the range of frequency of 20 Hz to 20 kHz, though this varies significantly with age, occupational hearing damage, and gender; some individuals are able to hear up to 22 kHz and perhaps beyond, while others are limited to about 16 kHz. Frequencies capable of being heard by humans are called audio or referred to as sonic. Frequencies higher than audio are referred to as ultrasonic, while frequencies below audio are referred to as infrasonic.
Resolution
The resolution of the converter indicates the number of discrete values it can produce. It is usually expressed in bits. For example, an ADC that encodes an analog input to one of 256 discrete values has a resolution of eight bits, since
28 = 256.
Resolution can also be defined electrically, and expressed in volts. The voltage resolution of an ADC is equal to its overall voltage measurement range divided by the number of discrete values. Some examples may help:
Example 1
Full scale measurement range = 0 to 10 volts
ADC resolution is 12 bits: 212 = 4096 quantization levels
ADC voltage resolution is: (10-0)/4096 = 0.00244 volts = 2.44 mV
Example 2
Full scale measurement range = -10 to +10 volts
ADC resolution is 14 bits: 214 = 16384 quantization levels
ADC voltage resolution is: (10-(-10))/16384 = 20/16384 = 0.00122 volts = 1.22 mV
In practice, the resolution of the converter is limited by the signal-to-noise ratio of the signal in question. If there is too much noise present in the analog input, it will be impossible to accurately resolve beyond a certain number of bits of resolution, the "effective number of bits" (ENOB). While the ADC will produce a result, the result is not accurate, since its lower bits are simply measuring noise. The S/N ratio should be around 6 dB per bit of resolution required.
Response type
Linear ADCs
Most ADCs are of a type known as linear, although analog-to-digital conversion is an inherently non-linear process (since the mapping of a continuous space to a discrete space is a non-invertible and therefore non-linear operation). The term linear as used here means that the range of the input values that map to each output value has a linear relationship with the output value, i.e., that the output value k is used for the range of input values from
m(k + b)
to
m(k + 1 + b),
where m and b are some constants. Here b is typically 0 or −0.5. When b = 0, the ADC is referred to as mid-rise, and when b = −0.5 it is referred to as mid-tread.
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Non-linear ADCs
If the probability density function of a signal being digitized is uniform, then the signal-to-noise ratio relative to the quantization noise is the best possible. Because of this, it's usual to pass the signal through its CDF before the quantization. This is good because the regions that are more important get quantized with a better resolution. In the dequantization process, the inverse CDF is needed.
This is the same principle behind the companders used in some tape-recorders and other communication systems, and is related to entropy maximization. (Never confuse companders with compressors!)
For example, a voice signal has a laplacian distribution. This means that the region around 0 carries more information than the regions with higher amplitudes. Because of this, logarithmic ADCs are very common in voiced communication systems to increase the dynamic range of the representable values while retaining fine-granular fidelity in the low-amplitude region.
An 8 bit a-law or the μ-law logarithmic ADC covers the wide dynamic range and has a high resolution in the critical low-amplitude region, that would otherwise require a 12 bit linear ADC.
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Accuracy
Accuracy depends on the error in the conversion. If the ADC is not broken, this error has two components: quantization error and (assuming the ADC is intended to be linear) non-linearity. These errors are measured in a unit called the LSB, which is an abbreviation for least significant bit. In the above example of an eight-bit ADC, an error of one LSB is 1/256 of the full signal range, or about 0.4%.
Quantization error is due to the finite resolution of the ADC, and is an unavoidable imperfection in all types of ADC. The magnitude of the quantization error at the sampling instant is between zero and half of one LSB.
In the general case, the original signal is much larger than one LSB. When this happens, the quantization error is not correlated with the signal, and has a uniform distribution. Its RMS value is the standard deviation of this distribution, given by . In the eight-bit ADC example, this represents 0.113 % of the full signal range.
All ADCs suffer from non-linearity errors caused by their physical imperfections, causing their output to deviate from a linear function (or some other function, in the case of a deliberately non-linear ADC) of their input. These errors can sometimes be mitigated by calibration, or prevented by testing.
Important parameters for linearity are integral non-linearity (INL) and differential non-linearity (DNL).
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Sampling rate
The analog signal is continuous in time and it is necessary to convert this to a flow of digital values. It is therefore required to define the rate at which new digital values are sampled from the analog signal. The rate of new values is called the sampling rate or sampling frequency of the converter.
A continuously varying bandlimited signal can be sampled (that is, the signal values at intervals of time T, the sampling time, are measured and stored) and then the original signal can be exactly reproduced from the discrete-time values by an interpolation formula. The accuracy is however limited by quantization error. However, this faithful reproduction is only possible if the sampling rate is higher than twice the highest frequency of the signal. This is essentially what is embodied in the Shannon-Nyquist sampling theorem.
Since a practical ADC cannot make an instantaneous conversion, the input value must necessarily be held constant during the time that the converter performs a conversion (called the conversion time). An input circuit called a sample and hold performs this task—in most cases by using a capacitor to store the analogue voltage at the input, and using an electronic switch or gate to disconnect the capacitor from the input. Many ADC integrated circuits include the sample and hold subsystem internally.
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Aliasing
All ADCs work by sampling their input at discrete intervals of time. Their output is therefore an incomplete picture of the behaviour of the input. There is no way of knowing, by looking at the output, what the input was doing between one sampling instant and the next. If the input is known to be changing slowly compared to the sampling rate, then it can be assumed that the value of the signal between two sample instants was somewhere between the two sampled values. If, however, the input signal is changing fast compared to the sample rate, then this assumption is not valid.
If the digital values produced by the ADC are, at some later stage in the system, converted back to analog values by a digital to analog converter or DAC, it is desirable that the output of the DAC be a faithful representation of the original signal. If the input signal is changing much faster than the sample rate, then this will not be the case, and spurious signals called aliases will be produced at the output of the DAC. The frequency of the aliased signal is the difference between the signal frequency and the sampling rate. For example, a 2 kHz sinewave being sampled at 1.5 kHz would be reconstructed as a 500 Hz sinewave. This problem is called aliasing.
To avoid aliasing, the input to an ADC must be low-pass filtered to remove frequencies above half the sampling rate. This filter is called an anti-aliasing filter, and is essential for a practical ADC system.
Although aliasing in most systems is unwanted, it should also be noted that it can be exploited to provide simultaneous down-mixing of a band-limited high frequency signal (see frequency-mixer).
Dither
In A to D converters, performance can be improved using dither. This is a very small amount of random noise (white noise) which is added to the input before conversion. Its amplitude is set to be about half of the least significant bit. Its effect is to cause the state of the LSB to randomly oscillate between 0 and 1 in the presence of very low levels of input, rather than sticking at a fixed value. Rather than the signal simply getting cut off altogether at this low level (which is only being quantized to a resolution of 1 bit), it extends the effective range of signals that the A to D converter can convert, at the expense of a slight increase in noise - effectively the quantization error is diffused across a series of noise values which is far less objectionable than a hard cutoff. The result is an accurate representation of the signal over time. A suitable filter at the output of the system can thus recover this small signal variation.
An audio signal of very low level (w.r.t. the bit depth of the ADC) sampled without dither sounds extremely distorted and unpleasant. Without dither the low level always yields a '1' from the A to D. With dithering, the true level of the audio is still recorded as a series of values over time, rather than a series of separate bits at one instant in time.
A virtually identical process, also called dither or dithering, is often used when quantizing photographic images to a fewer number of bits per pixel - the image becomes noisier but to the eye looks far more realistic than the quantized image, which otherwise becomes banded. This analogous process may help to visualize the effect of dither on an analogue audio signal that is converted to digital.
Dithering is also used in integrating systems such as electricity meters. Since the values are added together, the dithering produces results that are more exact than the LSB of the analog-to-digital converter.
Introduction to sampling
From a signal processing perspective, the theorem describes two processes; a sampling process, in which a continuous time signal is converted to a discrete time signal, and a reconstruction process, in which the continuous signal is recovered from the discrete signal.
Let us assume that the continuous signal varies over time and that the sampling process is done by simply measuring the continuous signal's value every T seconds, which is called the sampling interval (in practice, the sampling interval is typically quite small, on the order of milliseconds or even microseconds). This results in a sequence of numbers which can be said to represent the original signal in one way or another. Let us call the elements of this sequence samples. Notice that each sample is associated to the specific point in time where it was measured. Notice also that 1/T can be interpreted as a sampling frequency, which is often represented by the symbol fs and measured in samples per second, or equivalently, hertz.
Let us also assume that the reconstruction process is done by somehow interpolating a continuous/analog signal from the samples.
A very practical question would be to ask: under what circumstances is it possible to reconstruct the original signal completely and exactly (perfect reconstruction)?
The answer is provided by the sampling theorem. In fact, it states two things:
The sinc-function, showing the central location at x/π = 0, and periodicity of zero-crossings at non-zero, integer multiples of x/π.Each sample should be multiplied by a particular function, called a sinc-function. The width of each half-period of the sinc-function is scaled to match the sampling frequency, and the location of the sinc-function's central point is shifted to the time of that sample. All of these shifted and scaled functions are then added together to recover the original signal. Recall that a sinc-function is continuous, which means that the result of this operation is indeed a continuous signal. This procedure derives from the Nyquist-Shannon interpolation formula.
In order to obtain the original signal after this reconstruction process, we must also observe a critical condition on the sampling frequency. It must be at least twice as large as the highest frequency component of the original signal, also measured in hertz.
Sometimes, the sampling theorem refers only to the last statement, but you need also the first one to put things into the right context.
A few practical conclusions can be drawn from the theorem:
If it is known that the signal which we sample has a certain highest frequency, the theorem gives us the lowest possible sampling frequency to assure perfect reconstruction. This minimum value of the sampling frequency is called the Nyquist rate, or fN.
If instead the sampling frequency is known, the theorem gives us an upper bound for the frequencies of the signal to assure perfect reconstruction.
Both of these cases imply that the signal to be sampled should be bandlimited, i.e., any component of this signal which has a frequency above a certain bound should be zero, or at least sufficiently close to zero to allow us to neglect its influence on the resulting reconstruction. In the first case the condition of bandlimitation of the sampled signal can be accomplished by assuming a model of the signal which can be analysed in terms of the frequency components it contains, e.g., sounds which are made by a speaking human normally contains very small frequency components above 5 kHz and it is then sufficient to sample such an audio signal with a sampling frequency of at least 10 kHz. For the second case, we have to assure that the sampled signal is bandlimited such that frequency components above half of the sampling frequency can be neglected. This is usually accomplished by means of a suitable low-pass filter.
In practice, neither of the two statements of the sampling theorem described above can be completely satisfied. The reconstruction process which involves the sinc-functions can be described as ideal. It cannot be realized in practice since it implies that each sample contributes to the reconstructed signal at almost all time points. Instead some type of approximations of the sinc-functions which are truncated to limited intervals have to be used. The error which corresponds to the sinc-function approximation is referred to as interpolation error. Furthermore, in practice the sampled signal can never be exactly bandlimited. This means that even if an ideal reconstruction could be made, the reconstructed signal would not be exactly the sampled signal. The error which corresponds to the failure of bandlimitation is referred to as aliasing.
The sampling theorem implies that someone who is going to design a system which deals with sampling and reconstruction processes needs a thorough understanding of the signal to be sampled, in particular its frequency content, the sampling frequency, how the signal is reconstructed in terms of interpolation, and the requirement for the total reconstruction error, including aliasing and interpolation error. In simple terms, all these properties and parameters have to be carefully tuned in order to obtain a useful system.
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Aliasing
If the sampling condition is not satisfied, then frequencies will overlap (see the proof below). This overlap is called aliasing.
To prevent aliasing, two things can readily be done
Increase the sampling rate
Introduce an anti-aliasing filter or make anti-aliasing filter more stringent
The anti-aliasing filter is to restrict the bandwidth of the signal to satisfy the sampling condition. This holds in theory, but is not satisfiable in reality. It is not satisfiable in reality because a signal will have some energy outside of the bandwidth. However, the energy can be small enough that the aliasing effects are negligible.
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Application to multivariable signals and images
Subsampled image showing a Moiré pattern
See for full size image
The sampling theorem is usually formulated for functions of a single variable. Consequently, the theorem is directly applicable to time-dependent signals and is normally formulated in that context. However, the sampling theorem can be extended in a straightforward way to functions of arbitrarily many variables. Grayscale images, for example, are often represented as two-dimensional arrays (or matrices) of real numbers representing the relative intensity of each pixel located at the intersection of a single row and a single column. As a result, grayscale images require two independent variables, or indices, to specify each pixel uniquely – one for the row, and one for the column.
Color images typically consist of a composite of three separate grayscale images, one to represent each of the three primary colors – red, green, and blue, or RGB for short. So color images actually require three independent indices, the first two specifiy the pixel location, and the third specifies one of the three colors.
Similar to one-dimensional discrete-time signals, images can also suffer from aliasing if the sampling resolution, or pixel density, is inadequate. For example, a digital photograph of a striped shirt with high frequencies (in other words, the distance between the stripes is small), can cause aliasing between the shirt and the camera's sensor array. The aliasing appears as a Moiré pattern. The "solution" to higher sampling in the spatial domain for this case would be to move closer to the shirt or use a higher resolution sensor (for example, a CCD).
Another example is shown to the right in the brick patterns. The top image shows the effects when the sampling theorem is not followed. When software rescales an image (the same process that creates the thumbnail shown in the bottom image) it, in effect, runs the image through a low-pass filter first and then downsamples the image to result in a smaller image that does not exhibit the Moiré pattern. The top image is what happens when the image is downsampled without low-pass filtering and aliasing results.
The top image was created by zooming out in GIMP and then taking a screenshot of it. Likely reason that this works is that the zooming feature simply downsamples without low-pass filtering (probably for performance reasons) since the zoomed image is for on-screen display instead of printing or saving.
The application of the sampling theorem on images should not be made without care. For example, the sampling process in any standard image sensor (CCD or CMOS camera) is relatively far from the ideal sampling which would measure the image intensity at a single point. Instead these devices have a relatively large sensor area at each sample point in order to obtain sufficient amount of light. Also, it is not obvious that the analog image intensity function which is sampled by the sensor device is bandlimited. It should be noted, however, that the non-ideal sampling in itself implies some type of low-pass filtering, although far from one that effectively removes high frequency components. Furthermore, since the intensity function in practice is zero outside the actual sensor chip, it cannot be bandlimited. Despite that images have these problems in relation to the sampling theorem, it can be used to describe the basic aspects of down and up sampling of images, but only sufficiently far from the image boundaries.
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Downsampling
When a signal is downsampled, the theorem must still be satisfied in order to avoid aliasing. To meet the requirements of the theorem, the signal must pass through a low-pass filter of appropriate cutoff frequency prior to the downsampling operation. The low-pass filter, which prevents aliasing, is called an anti-aliasing filter.
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Critical frequency
The critical frequency is defined as twice the bandwidth of the signal. If the sampling frequency is at the critical frequency, exactly twice the highest frequency of the input signal, then phase mismatches between the sampler and the signal will distort the signal. For example, sampling cos(πt) at will give you the discrete signal cos(πn), as desired. However, sampling the same signal at will give you a constant zero signal. These two sets of samples, which differ only in phase and not frequency, give dramatically different results because they sample at the critical frequency, instead of strictly above the critical frequency. This result provides an rationale for the strict inequality of the sampling condition, and why the sampling rate must exceed the critical frequency.
High fidelity or hi-fi reproduction is a quality standard that means the reproduction of sound or images is very faithful to the original. High fidelity aims to achieve minimal or unnoticeable amounts of noise and distortion. The term high fidelity tends to be applied to any reasonable-quality home-music system, though some believe that a higher standard than this is intended, and in 1973, the German Deutsches Institut für Normung (DIN) standard DIN 45500 laid down mimimum requirements for measurements of frequency response, distortion, noise and other defects and gained some recognition in hi-fi magazines.
High-fidelity enthusiasts are often known as audiophiles. The equipment they prefer is often termed "high end."
Contents [hide]
1 History
2 Ascertaining high fidelity: double-blind tests
3 Semblance of realism
4 Modularity
5 Modern equipment
6 See also
7 External links
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History
The 1920s saw the introduction of electronic amplification, microphones, and the application of quantitative engineering principles to the reproduction of sound. Much of the pioneering work was done at Bell Laboratories and commercialized by Western Electric. Acoustically-recorded disc records with capriciously peaky frequency response were replaced with electrically-recorded records. The Victor Orthophonic phonograph, although purely acoustic, was created by engineers who applied waveguide technology to the design of the interior folded horn to produce a smooth frequency response which complemented and equalled that of the electrically-recorded Victor Orthophonic records.
Meanwhile, the rise of radio meant increased popularity for loudspeakers and tube amplifiers, so there was an anomaly of a period of time during which radio receivers commonly used loudspeakers and electronic amplifiers to produce sound, while phonographs were still commonly purely mechanical and acoustic. Later, electronic phonographs became available, as stand-alone units or designed to play through consumer's radios. The now ubiquitous RCA connector, was first introduced by the Radio Corporation of America for this purpose.
After World War II, several innovations created the conditions for a major improvement of home-audio quality:
the advent of the 33-1/3 RPM Long Play (LP) microgroove vinyl record, with low surface noise and quantitatively-specified equalization curves. Classical music fans, who were opinion leaders in the audio market quickly adopted LPs because, unlike with older records, most classical works would fit on a single LP.
FM radio, with wider audio bandwidth and less susceptibility to signal interference and fading than AM radio.
better amplifier designs, with more attention to frequency response and much higher power output capability, allowing audio peaks to be reproduced without distortion.
loudspeakers with separate sections for low and high frequencies ("woofers" and "tweeters"), connected via an audio crossover network, and more carefully engineered enclosures.
In the 1950s, the term high fidelity began to be used by audio manufacturers as a marketing term to describe records and equipment which were intended to provide faithful sound reproduction. While some consumer simply interpreted high fidelity as fancy and expensive equipment, many found the difference in quality between "hi-fi" and the then standard AM radios and 78 RPM records readily apparent and bought 33 LPs, such as RCA's New Orthophonics and London's ffrrs, and high-fidelity phonographs. Audiophiles paid attention to technical characteristics, and bought individual components, such as separate turntables, radio tuners, preamplifiers, power amplifiers and loudspeakers. Some enthusiasts assembled their own loudspeaker systems. In the 1950s, hi-fi became a generic term, to some extent displacing phonograph and record player. Rather than playing a record on the phonograph, people would play it on the hi-fi.
In the late 1950s and early 1960s, the development of the Westrex single-groove stereophonic record led to the next wave of home-audio improvement, and in common parlance, stereo displaced hi-fi. Records were now played on a stereo. In the world of the audiophile, however, high fidelity continued and continues to refer to the goal of highly-accurate sound reproduction and to the technological resources available for approaching that goal. A very popular type of system for reproducing music from the 1970s onwards is the integrated music centre--the successor to the older stereogram or radiogram. Purists will generally avoid referring to these systems as high fidelity, though some are capable of very good quality sound reproduction.
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Ascertaining high fidelity: double-blind tests
Double-blind testing has been required in the approval of new medicines since about 1960. Although single-blind testing of loudspeakers had been used for a number of years by Floyd E. Toole at the National Research Council of Canada, the double-blind audio listening test of amplifiers was first described in the United States by Daniel J. Shanefield in November of 1974 in the newsletter of the Boston Audio Society. This was later reported to the general public in High Fidelity magazine, March 1980. The double-blind listening comparison is now a standard procedure with almost all audio professionals respected in their field. For marketing purposes, a few manufacturers of very expensive audio equipment dispute the need for this test. A commonly-used improvement of this test is the ABX-listening comparison. This involves comparing two known audio sources (A and B) with either one of these when it has been randomly selected (X). The test and its associated equipment was developed by the Southeastern Michigan Woofer and Tweeter Marching Society (SMWTMS)--a semi-professional organization in Detroit that is very active in the double-blind testing of new audio components.
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Semblance of realism
When high fidelity was limited to monophonic sound reproduction, a realistic approximation to what the listener would experience in a concert hall was limited. Researchers early realized that the ideal way to experience music played back on audio equipment was through multiple transmission channels, but the technology was not available at that time. It was, for example, discovered that a realistic representation of the separation between performers in an orchestra from an ideal listening position in the concert hall would require at least three loudspeakers for the front channels. For the reproduction of the reverberation, at least two loudspeakers placed behind or to the sides of the listener were required.
Stereophonic sound provided a partial solution to the problem of creating some semblance of the illusion of performers performing in an orchestra by creating a phantom middle channel when the listener sits exactly in the middle of the two front loudspeakers. When the listener moves slightly to the side, however, this phantom channel disappears or is greatly reduced. An attempt to provide for the reproduction of the reverberation was tried in the 1970s through quadraphonic sound but, again, the technology at that time was insufficient for the task. Consumers did not want to pay the additional costs required in money and space for the marginal improvements in realism. With the rise in popularity of home theatre, however, multi-channel playback systems became affordable, and consumers were willing to tolerate the six to eight channels required in a home theatre. The advances made in signal processors to synthesize an approximation of a good concert hall can now provide a somewhat more realistic illusion of listening in a concert hall.
In addition to spatial realism, the playback of music must be subjectively free from noise to achieve realism. The compact disc (CD) provides at least 90 decibels of dynamic range, which is about as much as most people can tolerate in an average living room. This therefore requires the playback equipment to provide a signal-to-noise ratio of at least 90 decibels. Many people can hear up to, at most, 15 kHz and for a few, up to 20 kHz. There is relatively little music below 50 Hz, loud bass below 30 Hz is rare, music below 16 Hz is almost non-existent, and music below 5 Hz is probably non-existent. (Incidentally, the cannons in Telarc's recording of Pyotr Tchaikovsky's 1812 Overture are said to go down to 5 Hz.) The equipment must also provide no noticeable distortion of the signal or emphasis or de-emphasis of any frequency in this frequency range. Except for spatial realism, good modern equipment can easily satisfy all of these requirements at a relatively moderate cost.
Modularity
Integrated, midi, or lifestyle systems contain one or more sources such as a CD player, a tuner, or a cassette deck together with a preamplifier and a power amplifier in one box. (Midi has no connection with MIDI technology in electronic instruments.) Such products are generally disparaged by audiophiles, although some high-end manufacturers produce integrated systems. The traditional hi-fi enthusiast, however, will build a system from separates, often with each item from a different manufacturer specialising in a particular component. This provides the most flexibility for piece-by-piece upgrades.
For slightly less flexibility in upgrades, a preamplifier and a power amplifier in one box is called an integrated amplifier; with a tuner, it is a receiver. A monophonic power amplifier is a monoblock. Other modules in the system may include components like cartridges, tonearms, turntables, DVD players that play a wide variety of discs including CDs, CD recorders, MiniDisc recorders, hi-fi video-cassette recorders (VCRs), reel-to-reel recorders, equalizers, signal processors, and subwoofers.
This modularity allows the enthusiast to spend as little or as much as he wants on a component that suits his specific needs. In a system built from separates, sometimes a failure on one component still allows partial use of the rest of the system. A repair of an integrated system, though, means complete lack of use of the system. Another advantage of modularity is the ability to spend one's money on only a few core components at first and then later add additional components to one's system. Because of all these advantages to the modular way of building a high-fidelity system instead of buying an integrated system, audiophiles almost always assemble their system from separates. Some of the obvious disadvantages of this approach are increased cost, complexity, and space required for the components.
For neuroscientists, the first thing to point to is the brain; whales have enormous brains and bird brains undergo neurogenesis every year in the area which retains song memory. Monkeys and dolphins must not have the neural equipment or the need to develop music. The question of neural ability now befalls the third species with musical ability: Homo sapiens. What enables us to create and enjoy music which is absent in dolphins and monkeys? Will these structures be found in whales and/or birds? Or did our musical talents evolve separately?
We, as humans, love resonant frequencies; they are present in all wave forms in physics, but when they are aurally perceived we call them keys or pitches. In western music there are twelve chromatic pitch classes, which can be represented in many ways.
In eastern music there are more notes than in the west. For instance, in western music there is only one interval between a C and a C sharp; but in India this is a large jump and there are many keys in between what we call a C and a C sharp. Each of these pitches can be arranged into chords, which occur in characteristic sequences throughout Western music. For example, after two or three of these often-used chords are heard in sequence, the sequence of chords can be satisfactorily resolved only by a limited number of expected chords. This is a particular area of interest, as even musical laymen can detect these chord patterns and recognize when a chord progression has not resolved "correctly". For instance, a 1, 4, 5, 1 progression sounds pleasant, but if the sequence ended instead with a 3 chord, the error would be immediately obvious.
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