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[[Image:Ii-V-I turnaround in C.png|thumb|300px|Tonic (I) in [[ii-V-I turnaround]] on C, found at the end of the [[circle progression]] {{audio|Ii-V-I turnaround in C.mid|Play}}]]
[[Image:Major seventh chord on C.png|thumb|right|Major seventh chord on C {{audio|Major seventh chord on C.mid|Play}}. I<sup>7</sup> or '''tonic seventh chord''' in C major.<ref name="B&S">Benward & Saker (2003). ''Music: In Theory and Practice, Vol. I'', p.229. Seventh Edition. ISBN 978-0-07-294262-0.</ref>]]
[[Image:Minor seventh chord on C.png|thumb|right|Minor-minor (i<sup>7</sup>) seventh chord on C in natural minor.<ref name="B&S 230">Benward & Saker (2003), p.230.</ref> {{audio|Minor seventh chord on C.mid|Play}}]]
[[Image:Minor major seventh chord on C.png|thumb|right|Minor major seventh chord on C.<br/> i<math>_M^7</math> in C [[Minor scale#Harmonic and melodic minor|harmonic]] or [[minor scale|ascending melodic minor]].<ref name="B&S 230"/> {{audio|Minor major seventh chord on C.mid|Play}}]]
[[Image:Minor 6-9 chord on C.png|thumb|right|Tonic minor [[6/9 chord]] on C, featuring the raised sixth degree of the [[jazz minor scale|ascending melodic minor]].<ref>Berg, Shelly (2005). ''Alfred's Essentials of Jazz Theory'', Book 3, p.90. ISBN 978-0-7390-3089-9.</ref> {{audio|Minor 6-9 chord on C.mid|Play}}]]
 
In [[music]], the '''tonic''' is the first [[degree (music)|scale degree]] of a [[diatonic scale]] and the [[tonality|tonal]] center or final [[resolution (music)|resolution]] tone.<ref>Benward & Saker (2003), p.33.</ref> The [[triad (music)|triad]] formed on the tonic note, the '''tonic chord''', is thus the most significant [[chord (music)|chord]]. More generally, the '''tonic''' is the [[pitch (music)|pitch]] upon which all other pitches of a piece are [[hierarchy|hierarchically]] referenced. Scales are named after their tonics, thus the tonic of the scale of C is the note C.
 
{{quote|In very much conventionally tonal music, harmonic [[musical analysis|analysis]] will reveal a broad prevalence of the [[primary triad|primary]] (often triadic) [[harmony|harmonies]]: tonic, dominant, and [[subdominant]] (i.e., I and its chief auxiliaries a 5th removed), and especially the first two of these.|Berry (1976)<ref name="Berry">Berry, Wallace (1976/1987). ''Structural Functions in Music'', p.62. ISBN 0-486-25384-8.</ref>}}
 
The '''tonic''' is often confused with the [[root (chord)|root]], which is the reference note of a chord, rather than that of the scale. It is also represented with the [[Roman numeral analysis|Roman numeral]] I.
 
==Importance and function==
In western European tonal [[music]] of the 18th and 19th centuries, the tonic center was the most important of all the different tone centers which a [[composer]] used in a piece of music, with most pieces beginning and ending on the tonic, usually modulating to the [[dominant (music)|dominant]] (the fifth above the tonic, or the fourth note down from the tonic) in between.
 
Two [[parallel key]]s have the same tonic.  For example, in both C major and C minor, the tonic is C.  However, [[relative key]]s  (two different scales that share a [[key signature]]) have different tonics. For example, C major and A minor share a key signature that feature no sharps or flats, despite having different tonic pitches (C and A, respectively).
 
{{anchor|Tone center}}''Tonic'' may be reserved exclusively for use in tonal contexts while '''''tonal center''''' and/or '''''pitch center''''' may be used in post- and atonal music: "For purposes of non-tonal centric music, it might be a good idea to have the term 'tone center' refer to the more general class of which 'tonics' (or tone centers in tonal contexts) could be regarded as a subclass."<ref>Berger (1963), p. 12. cited in Swift, Richard. "[http://www.jstor.org/stable/832876 A Tonal Analog: The Tone-Centered Music of George Perle]", p.258. ''Perspectives of New Music'', Vol. 21, No. 1/2, (Autumn, 1982 - Summer, 1983), pp. 257-284.</ref> Thus a pitch center may function referentially or contextually in an [[atonal]] context, often acting as axis or line of symmetry in an [[interval cycle]].<ref>{{Cite book|last=Samson |first=Jim |title=Music in transition: a study of tonal expansion and atonality, 1900-1920 |publisher=[[W. W. Norton & Company]] |location=[[New York City]] |year=1977 |pages= |isbn=0-393-02193-9 |oclc=3240273}}{{Page needed|date=September 2010}}</ref> '''''Pitch centricity''''' was coined by [[Arthur Berger]] in his "Problems of Pitch Organization in Stravinsky".<ref>{{Cite journal|doi=10.2307/832252 |last=Berger |first=Arthur |authorlink=Arthur Berger |date=Fall–Winter 1963 |title=Problems of Pitch Organization in Stravinsky |journal=[[Perspectives of New Music]] |volume=2 |issue=1 |pages=11–42|jstor=832252}}</ref>
 
The '''tonic''' [[diatonic function]] includes four separate activities or roles as the principal goal tone, initiating event, generator of other tones, and the stable center neutralizing the tension between dominant and subdominant.
 
==See also==
* [[Final (music)]]
 
==References==
{{Reflist}}
 
{{degrees|state=uncollapsed}}
{{chords}}
 
{{DEFAULTSORT:Tonic (Music)}}
[[Category:Scale degrees|1]]
[[Category:Diatonic functions]]

Revision as of 14:51, 3 February 2014

Tonic (I) in ii-V-I turnaround on C, found at the end of the circle progression My name: Lindsey Gavin
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Minor major seventh chord on C.
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In music, the tonic is the first scale degree of a diatonic scale and the tonal center or final resolution tone.[4] The triad formed on the tonic note, the tonic chord, is thus the most significant chord. More generally, the tonic is the pitch upon which all other pitches of a piece are hierarchically referenced. Scales are named after their tonics, thus the tonic of the scale of C is the note C.

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The tonic is often confused with the root, which is the reference note of a chord, rather than that of the scale. It is also represented with the Roman numeral I.

Importance and function

In western European tonal music of the 18th and 19th centuries, the tonic center was the most important of all the different tone centers which a composer used in a piece of music, with most pieces beginning and ending on the tonic, usually modulating to the dominant (the fifth above the tonic, or the fourth note down from the tonic) in between.

Two parallel keys have the same tonic. For example, in both C major and C minor, the tonic is C. However, relative keys (two different scales that share a key signature) have different tonics. For example, C major and A minor share a key signature that feature no sharps or flats, despite having different tonic pitches (C and A, respectively).

<Tone center>...</Tone center>Tonic may be reserved exclusively for use in tonal contexts while tonal center and/or pitch center may be used in post- and atonal music: "For purposes of non-tonal centric music, it might be a good idea to have the term 'tone center' refer to the more general class of which 'tonics' (or tone centers in tonal contexts) could be regarded as a subclass."[5] Thus a pitch center may function referentially or contextually in an atonal context, often acting as axis or line of symmetry in an interval cycle.[6] Pitch centricity was coined by Arthur Berger in his "Problems of Pitch Organization in Stravinsky".[7]

The tonic diatonic function includes four separate activities or roles as the principal goal tone, initiating event, generator of other tones, and the stable center neutralizing the tension between dominant and subdominant.

See also

References

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In computational complexity theory, the potential method is a method used to analyze the amortized time and space complexity of a data structure, a measure of its performance over sequences of operations that smooths out the cost of infrequent but expensive operations.[8][9]

Definition of amortized time

In the potential method, a function Φ is chosen that maps states of the data structure to non-negative numbers. If S is a state of the data structure, Φ(S) may be thought of intuitively as an amount of potential energy stored in that state;[8][9] alternatively, Φ(S) may be thought of as representing the amount of disorder in state S or its distance from an ideal state. The potential value prior to the operation of initializing a data structure is defined to be zero.

Let o be any individual operation within a sequence of operations on some data structure, with Sbefore denoting the state of the data structure prior to operation o and Safter denoting its state after operation o has completed. Then, once Φ has been chosen, the amortized time for operation o is defined to be

where C is a non-negative constant of proportionality (in units of time) that must remain fixed throughout the analysis. That is, the amortized time is defined to be the actual time taken by the operation plus C times the difference in potential caused by the operation.[8][9]

Relation between amortized and actual time

Despite its artificial appearance, the total amortized time of a sequence of operations provides a valid upper bound on the actual time for the same sequence of operations. That is, for any sequence of operations , the total amortized time is always at least as large as the total actual time . In more detail,

where the sequence of potential function values forms a telescoping series in which all terms other than the initial and final potential function values cancel in pairs, and where the final inequality arises from the assumptions that and . Therefore, amortized time can be used to provide accurate predictions about the actual time of sequences of operations, even though the amortized time for an individual operation may vary widely from its actual time.

Amortized analysis of worst-case inputs

Typically, amortized analysis is used in combination with a worst case assumption about the input sequence. With this assumption, if X is a type of operation that may be performed by the data structure, and n is an integer defining the size of the given data structure (for instance, the number of items that it contains), then the amortized time for operations of type X is defined to be the maximum, among all possible sequences of operations on data structures of size n and all operations oi of type X within the sequence, of the amortized time for operation oi.

With this definition, the time to perform a sequence of operations may be estimated by multiplying the amortized time for each type of operation in the sequence by the number of operations of that type.

Example

A dynamic array is a data structure for maintaining an array of items, allowing both random access to positions within the array and the ability to increase the array size by one. It is available in Java as the "ArrayList" type and in Python as the "list" type. A dynamic array may be implemented by a data structure consisting of an array A of items, of some length N, together with a number n ≤ N representing the positions within the array that have been used so far. With this structure, random accesses to the dynamic array may be implemented by accessing the same cell of the internal array A, and when n < N an operation that increases the dynamic array size may be implemented simply by incrementing n. However, when n = N, it is necessary to resize A, and a common strategy for doing so is to double its size, replacing A by a new array of length 2n.[10]

This structure may be analyzed using a potential function Φ = 2n − N. Since the resizing strategy always causes A to be at least half-full, this potential function is always non-negative, as desired. When an increase-size operation does not lead to a resize operation, Φ increases by 2, a constant. Therefore, the constant actual time of the operation and the constant increase in potential combine to give a constant amortized time for an operation of this type.

However, when an increase-size operation causes a resize, the potential value of n prior to the resize decreases to zero after the resize. Allocating a new internal array A and copying all of the values from the old internal array to the new one takes O(n) actual time, but (with an appropriate choice of the constant of proportionality C) this is entirely cancelled by the decrease of n in the potential function, leaving again a constant total amortized time for the operation.

The other operations of the data structure (reading and writing array cells without changing the array size) do not cause the potential function to change and have the same constant amortized time as their actual time.[9]

Therefore, with this choice of resizing strategy and potential function, the potential method shows that all dynamic array operations take constant amortized time. Combining this with the inequality relating amortized time and actual time over sequences of operations, this shows that any sequence of n dynamic array operations takes O(n) actual time in the worst case, despite the fact that some of the individual operations may themselves take a linear amount of time.[9]

Applications

The potential function method is commonly used to analyze Fibonacci heaps, a form of priority queue in which removing an item takes logarithmic amortized time, and all other operations take constant amortized time.[11] It may also be used to analyze splay trees, a self-adjusting form of binary search tree with logarithmic amortized time per operation.[12]

References

43 year old Petroleum Engineer Harry from Deep River, usually spends time with hobbies and interests like renting movies, property developers in singapore new condominium and vehicle racing. Constantly enjoys going to destinations like Camino Real de Tierra Adentro. Template:Chords

Warning: Default sort key "Tonic (Music)" overrides earlier default sort key "Potential Method".

  1. Benward & Saker (2003). Music: In Theory and Practice, Vol. I, p.229. Seventh Edition. ISBN 978-0-07-294262-0.
  2. 2.0 2.1 Benward & Saker (2003), p.230.
  3. Berg, Shelly (2005). Alfred's Essentials of Jazz Theory, Book 3, p.90. ISBN 978-0-7390-3089-9.
  4. Benward & Saker (2003), p.33.
  5. Berger (1963), p. 12. cited in Swift, Richard. "A Tonal Analog: The Tone-Centered Music of George Perle", p.258. Perspectives of New Music, Vol. 21, No. 1/2, (Autumn, 1982 - Summer, 1983), pp. 257-284.
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  9. 9.0 9.1 9.2 9.3 9.4 Template:Introduction to Algorithms
  10. Goodrich and Tamassia, 1.5.2 Analyzing an Extendable Array Implementation, pp. 139–141; Cormen et al., 17.4 Dynamic tables, pp. 416–424.
  11. Cormen et al., Chapter 20, "Fibonacci Heaps", pp. 476–497.
  12. Goodrich and Tamassia, Section 3.4, "Splay Trees", pp. 185–194.