I Kissed a Girl - Wikipedia
alphabetically. New and popular versions of Katy Perry easy to print and share. If We Ever Meet Again Ukulele. (1 version) Not Like The Movies Ukulele. The Complete History of Katy Perry & Taylor Swift's Complicated . Were we ever so young? Back in the days of “Love Story” and “I Kissed a Girl” success, the two She hits the hidden chord in them [where they say], 'Oh my God, I've . I have bad days when I don't want to go to a photo shoot, but I'm not. Note, Alternative (maybe easier) version: Use the transposer to turn the chords 1 half step(s) down. - Put a capo on fret 1 (if you want to stay in the same key).
There is a simple algorithm for finding the Root of a partial overtone series: Given a set of tones, hear the approximate Greatest Common Divisor gcd of the tones as the fundamental. When I was in graduate school, my advisor, Mike Posner, told me about the work of a graduate student in biology, Petr Janata Peter [sic] placed electrodes in the inferior colliculus of the barn owl, part of its auditory system. Then, he played the owls a version of Strauss's "The Blue Danube Waltz" made up of tones [by "tones" here he means what we are calling "notes": Petr hypothesized that if the missing fundamental is restored at the early levels of auditory processing, neurons in the owl's inferior colliculus should fire at the rate of the missing fundamental.
This was exactly what he found. And because the electrodes put out a small electrical signal with each firing -- and because the firing rate is the same as a frequency of firing -- Petr sent the output of these electrodes to a small amplifier, and played back the sound of the owl's neurons through a loudspeaker. What he heard was astonishing; the melody of "The Blue Danube Waltz" sang clearly from the loudspeakers: We were hearing the firing rates of the neurons and they were identical to the frequency of the missing fundamental.
The harmonic series has an instantiation not just in the early levels of auditory processing, but in a completely different species. I passed this on to Daniel J.
1 The Problem of Music
Levitin; his response [Levitin, 24 May ]: You're absolutely right that these two possibilities need to be distinguished. The electrodes that were placed in the brain of the owl in the inferior colliculus were analyzed using specotrograms[sic] and fourier[sic] analysis.
It was clear that the signal itself coming from the owl's brain had replaced the missing fudnamental[sic]. It was only after this analysis that Petr thought to hook it all up to play the signal over loudspeakers so that humans could hear the output as a cool demonstration. Female Mosquitoes only mate when rate of the wing-beats of the male harmonize at a Perfect Fifth above the rate of her wing-beats we start introducing musical terminology such as the Perfect Fifth in Section 3.
From "Mosquitoes make sweet love music" [ Mosquito-harmony ]: The familiar buzz of a flying female mosquito may be irritating to humans, but for her male counterpart, it is an irresistible mating signal.
Males and females each have their own characteristic flight tone - which they create by beating their wings. But when scientists from Cornell University listened in on a male Aedes aegypti pursuing his mate, they were surprised to hear a new kind of "music" playing The amorous couple began to beat their wings together at a matching frequency - 1, hertz.
This love song is a "harmonic", or multiple, of their individual frequencies - Hz for the female and Hz for the male It could be his odour[sic], or his bright black and white markings. Evolution has no time to waste and therefore these resources are likely used in an optimal way -- or at the very least any easy optimizations will have been done for a given organization of a brain. That is, evolution will drive a machine into a local optimum, even if it gets stuck there and does not reach a global optimum.
Having separate hardware in the brain for recognizing each combination of tones that co-occur in nature is sub-optimal and it would just be an expensive way to use up neurons. The algorithm every engineer resorts to in this situation, and what I suspect the brain does also, is to find a way to "re-use code": Here, we want one Harmonic Series recognizer that works for all the different overtone series-es we may encounter.
Further, the problem that the brain is solving when listening to music is recognizing sounds that are important to it, such as perhaps the nuances of a human voice against a background of noise. In order to recognize something, it is ok to simplify the input or throw away information if it makes the problem easier, as long as enough information is retained to complete the task.
We now consider two different tricks for greatly simplifying the computation the brain must do in order to recognize the harmonic series. We will also conjecture some computational artifacts of the way the brain computes that should result from these optimizations, resulting in well-known universal features of music: Differences Between Sounds Again, most engineers would tell you that, given the problem of designing a brain to recognize the Harmonic Series, their intuition would tell them to build one, single Harmonic Series recognizer, not a different one for every possible note.
The way to accomplish this would be to make the machine recognize only that which is the same or mostly the same in all overtone series-es and ignore that which changes.
While the tones of different Harmonic Series-es differ, conveniently the ratio of their frequencies to their fundamental frequency does not. Therefore we consider it very likely that Conjecture Four: The brain normalizes tones by dividing tones to get tone ratios. Recognizing ratios of tones and notes more strongly than the absolute tones themselves is a phenomenon called "Relative Pitch" [ rel ]. A ratio of a pair of tones or notes is called an "interval".
Sounds Normalized to a Factor of Two Processing sound requires operating on frequencies over several orders of magnitude.
If these frequencies could be made to "wrap-around" then we have another opportunity for code re-use.
When the police take a mug shot of a criminal, their goal is to take the photo in such a way as to maximize the recognizability of the subject in the future given the photo. They employ a common trick used in the recognition problem: We say they normalize the photograph: Consider the conceptually straightforward process of the brain halving or doubling the frequency of a wave until it is within a particular range.
Now the brain only needs a Harmonic Series recognizer for tones within a frequency range of a single factor of two, not across the whole spectrum of sound. Breaking the problem into two parts like this, 1 normalization followed by 2 recognition, greatly simplifies the resulting frequency recognizer.
We therefore consider it likely that Conjecture Five: The brain normalizes tones by halving or doubling them until within a particular frequency range spanned by a factor of two. The individual computational units of the brain are not as fast as those in modern electronics, however those of the brain are operating in "massive parallel": If any one matches, the harmonic has been found.
If this were so, then tones and notes that differ from each other by a factor of two would sound very much alike. The range of notes that are all within one factor of two is called in music an "Octave" [ oct ]. Here is a fundamental quality of music. Note names repeat because of a perceptual phenomenon that corresponds to the doubling and halving of frequencies. When we double or halve a frequency, we end up with a note that sounds remarkably similar to the one we started out with. This relationship, a frequency ratio of 2: It is so important that, in spite of the large differences that exist between musical cultures -- between Indian, Balinese, European, Middle Eastern, Chinese, and so on -- every culture we know of has the octave as the basis for its music, even if it has little else in common with other musical traditions.
Again, according to Levitin, the Octave interval occurs in every musical tradition in the world. This observation is the first of many to suggest that the musicality of sound depends on something universal about human beings, rather than simply being learned from culture. Sweetness is the Ideal Recall from Section 1. Recall from the same section that Levitin suggests that we use this timbre to solve the important problem of recognizing people and their emotional state.
When Court Jones, the Golden Nosey winner, describes how he teaches the craft to younger artists, he lays out exactly the algorithm that vision scientists believe humans use to identify faces. Students, he says, should imagine a generic face and then notice how the subject deviates from it: The same principle explains why the person at the convention with maybe the least symmetrical of faces appears by week's end in no fewer than 33 works of art on the ballroom walls.
I don't think I need a citation to claim that Katy Perry and Brad Pitt are considered to be very beautiful people. This suggests another conjecture. Absence of distortion or personality or timbre is sweetness.
Systematic Distortions from the Ideal Harmonic Series" above we saw that the overtone series of a single instrument is easily distorted by myriad physical effects. However, recall that for the same kind of instrument, those distortions were systematic and reliable.
Therefore by playing multiple notes, on instruments having the same or similar timbre, and relying on Relative Pitch to subtract the differences for us, from distorted overtone series-es we can magically recreate parts of the ideal Harmonic Series!
Per O'Donnell's comment in Section 1. Systematic Distortions from the Ideal Harmonic Series" above, since piano strings are not the strings of ideal physics, they don't make an ideal Harmonic Series. Instead, each tone in the series is moved by being multiplied by some fudge factor. However notice that strings on the piano are made of the same stuff, at least nearby strings, and this fudge factor should therefore be somewhat consistent across strings. That is, two corresponding tones at the same point in the overtone series of two different notes should get multiplied by the same fudge.
Tones of 1st note: Now notice that there are two kinds of intervals of tone pairs: Systematic Distortions from the Ideal Harmonic Series", real instruments can systematically produce overtones at frequencies different from those of the ideal Harmonic Series; one such instrument is the piano which produces stretched overtones. However, these distortions from the ideal Harmonic Series affect these horizontal and vertical intervals differently: Horizontal intervals are fudged: In Demo 31, a piece by Bach is played on computer-generated piano part 1 having normal overtones and part 4 having overtones where an Octave is stretched from a factor of 2 to a factor of 2.
Taken naively, our theory that the purity of vertical intervals matters to the brain suggests that these should both harmonize; however the normal one part 1 certainly sounds better.
Teenage Dream (Katy Perry song)
We suggest therefore that if the horizontal intervals are distorted grossly enough, then the fact that the vertical intervals are pure cannot save the harmony from being destroyed by the dissonance of the horizontal intervals. Systematic Distortions from the Ideal Harmonic Series", real instruments can systematically produce overtones at amplitudes different from those of the ideal Harmonic Series; one such instrument is the clarinet which emphasizes the odd overtones.
Again however, these distortions of the ideal Harmonic Series affect these horizontal and vertical intervals differently: Horizontal intervals are sometimes made by a pair of tones having unbalanced amplitudes: Vertical intervals are always made by a pair of tones having balanced amplitudes: Horizontal intervals are only one of each kind, a Whitman's Sampler: Vertical intervals are all of the same kind, an entire box of chocolate almond cherry: Again, for an introduction to musical intervals such as the Fifthsee Section 3.
Further these tone ratios are pure, have balanced amplitudes, and are all of the same interval. This harmonic effect works best if the two notes of an interval are played on the same instrument having therefore the same distortions from the ideal Harmonic Series.
My Men's Chorale teacher Bill Ganz told us that to have our voices harmonize, we should sing the same vowels, which supports this theory as the same vowels will have closer timbres [Ganz, c.
Notice that this effect allows instruments making tones that are not anywhere near the Harmonic Series to still harmonize with each other at least up to a point where the horizontal intervals interfere too much; see the point about [acoustical-demo, Demo 31] in Section 2. The wall of vertical intervals hammer the same relative pitch sensor with a wall of the pure interval one of the features of the cartoon physics ideal Harmonize Series of your brain is looking for.
Recall from the introduction to Section 2 "Living in a Computational Cartoon" the effect of pantyhose making a leg look rounder than round; again more on this effect in Section 2. Harmony is sweeter than sweet. It's impossibly sweet -- impossible for one voice anyway -- which is just what the theory predicts. Just Enough Complexity Anticipation and prediction is one of the fundamental operations of the brain.
We suggest that there is an art to balancing the simplicity and complexity: As we discuss below, 1 simplicity comes from data having a "theme" and 2ambiguity is the absence of a single explanation or theme and therefore a good way to rapidly produce complexity.
For example, people who speak more than one language sometimes have the experience of hearing words 1 in a language that they know, but 2 that they were not expecting, and therefore not understanding those words until they "listen" to them again in their mind from within the context of the language in which those words were spoken. There are myriad examples of context influencing how something occurs to someone.
The technical name for the amount of expected information one gets from situation is the entropy [ ent ] [ Wilkerson-entropy ].
If We Ever Meet Again - Wikipedia
Some call the entropy of a measurement the amount of surprise one expects get out of it. Clearly, if one knows more about what to expect in a situation, the amount of surprise can be greatly reduced. Since it is work to process information, we suggest that the brain likes to have reliable expectations in order to minimize the amount of surprise it is dealing with all day.
Life is full of situations where we may observe the consequences of a situation but are not told explicitly what is the state of the situation. There is nothing left to do but to infer a model of the state of affairs from observation of many details, and therefore inference is likely a constant activity of the brain. For example, people often infer the rules of a game from observation and without reading the rules.
Have you ever seen someone color-coordinate their clothes or even their room? Have you ever been to a "theme party" where everyone was to dress and act from a given era or situation? How about a "theme restaurant" or "theme park"? Having a theme for all of the elements of a given situation surprise reduction reduces the amount of new information or "surprise" that each one introduces, and ease of inference allows the brain to construct a whole from the parts.
Differences and changes are interesting to the brain, but too much difference fails to feel "unified" -- it does not all occur as parts of a whole. In support of both surprise-reduction and ease of inference, we consider it likely that Conjecture Seven: The brain wants input to have a theme. That is, the brain both infers themes from input and uses themes as context when processing input. In "From Molecule to Metaphor", Jerome Feldman, both a Computer and Cognitive Scientist, points out how much of the brain's processing of sentences is devoted to disambiguation and how easy it is to tease the brain by using ambiguous inputs that resolve in an unusual way.
Please read the following sentence aloud slowly, word by word: Sentences like these are called garden-path sentences because, in slow reading, we often notice that we have followed an analysis path that turned out to be wrong But why are people surprised in garden-path situations? The brain is a massively parallel information processor and is able to retain multiple active possibilities for interpreting sentence, scene, and so on.
Well, there must be a cutoff after which some possible interpretations are deemed so unlikely as to be not worth keeping active. We experience surprise when the analysis needed for a full sentence is one that was deactivated earlier as unlikely. This is a complex computational model, but nothing simpler can capture all the necessary interactions. The input the brain gets as we live life is inherently and often wildly ambiguous. Alternatives multiply and so the number possible ambiguities in a situation can easily grow exponentially.
No machine can keep up with the demands of a problem the size of which grows that fast. Much of the brain is a massive disambiguation engine that is running all the time and is functioning at its computational limit. Story plots are often of this form as well, in particular mysteries. The language of Shakespeare is full of double meanings and even perhaps a triple meaning here and there.
These are all to the same purpose: The brain enjoys having its disambiguation engine teased. Feature Vectors I need to introduce yet another computational idiom: It is actually a completely straightforward idea that you already use every day. Think of how you summarize a thing when you post an online ad to sell it. Suppose you are selling a car. You might very well put in the ad the total volume of the cylinders in the engine. Your probably won't list the number of bolts in the engine.
You probably will list how many miles the engine has driven. You probably will not list the number of hours the radio has been on even if you knew it. The point is that Humans naturally abstract; that is, they retain the features that are important for a given purpose and discard the rest.
All language is abstraction. Suppose I point at a chair and I say "what is that? An abstraction is a reduced amount of information that still serves the purpose.
In the context of recognizing a thing as a member of a class, an abstract adjective is called a "feature". Usually there is more than one, so we collect them together into a "vector", which just means a list where the elements are not interchangeable that is, you can't swap the mileage and the year of a car without severely changing the meaning of the car ad.
Once we have described a class of inputs as a vector of features, we have a clear algorithm for recognizing a thing as being a member of that class: Whenever we encounter a thing, for each feature in parallelcheck if that feature is present.
If all or most of the features in the vector are present "fire"then recognize the thing as being in the class abstracted by the feature "fire" the whole recognizer. Note that the second part above which looks for the conjunction of features may be realized by a more sophisticated mechanism than a simple AND gate that just fires its output when all of its inputs have fired: For example, even plants such as the Venus Fly Trap can compute a rather sophisticated conjunction of features before recognizing a fly [ venus-fly ]: The trapping mechanism is so specialized that it can distinguish between living prey and non-prey stimuli such as falling raindrops; two trigger hairs must be touched in succession within 20 seconds of each other or one hair touched twice in rapid succession, whereupon the lobes of the trap will snap shut in about 0.
Recall that in the case of virtual pitch, the feature recognition mechanism seems to find the greatest common divisor of the tones presented; that is, this recognizer uses a special wholistic property of this particular set of features in order to work well in the face of missing features. Recall that a timbre amounts to the systematic absence of parts of the idea Harmonic Series and that real sounds in particular, voices exhibit a range of timbres; thus the Harmonic Series recognizer must be able to robustly find the fundamental even when some of the tones are missing.
Systematic Distortions from the Ideal Harmonic Series". This is fun for a while, but it can get old. I don't often need huge lists of numbers added, but I really would like to go to an online auction site and find a car that is "sort of" like my ideal car which I might be willing to describe. You will notice the use of the non-crisp or "soft" phrase "sort of" in the previous problem specification. Some people try to get machines to do this sort of soft reasoning that humans do so well.
We were both so over it we just called it a night. She explained, "I thought about my own adolescent years, my own first love. I thought about watching Baz Luhrmann 's ' Romeo and Juliet ' and putting on a little mini disco ball light and just dreaming of Leo.
It is a very descriptive word; it packs a lot of emotion and imagery into three syllables I couldn't believe after all of our agonizing over 'youth' themes, that we had overlooked such an obvious one — the teenage condition. McKee tried to approach Luke about her idea, but he was upset about the amount of time he had spent working in the chorus, so he banned them from changing it. They started working on the verses, where Perry had already prepared most of the imagery.
And also it kind of exudes this euphoric feeling because everybody remembers what their teenage dreams were — all the girls that were on your poster walls. The chorus was rewritten, and the line "Skin tight jeans" was taken from the early "trying me on" version. When the final version was finished, McKee said, "We were all so pumped that it had paid off. I remember Max sitting back and saying 'I wish we could bottle this feeling'. It was really magical. It's intense being in love and being a teenager.
She added, "To me, this year is pretty heavy. I am going to be getting married and putting out this record, and there is so much going on that it's nice to think of those young dreams. According to James Montgomery of MTV, the lyrics refer to being in love, and about the feelings of commitment and security that it brings. He focused on two factors: At the same time, however, Perry begins singing the melody on that note, and returns to it frequently, even when it clashes with the dominant V chord as she sings "feel like I'm living a".
Her voice is the sun and the song is in orbit around it," he concludes. This contributes to the feeling of suspension that I mentioned above. As the jewel thief leaves the police station, the art thief is waiting for him outside, and the two join forces dressed in black clothes and masks to steal the original necklace and painting from the beginning of the video.
Chart performance[ edit ] In the Republic of Ireland, "If We Ever Meet Again" entered at number 15 on January 28,and week later rose to a current peak of number three. Similarly, in the United Kingdom, the single entered at number 17 on January 31,and one week later jumped to its peak of number three. The song was at number one for 4 weeks, before it was replaced at number one by J.
Williams with " You Got Me ". The song originally peaked on the Billboard Hot at number 98, but then re-entered at number 96 on the issue date April 10, It has peaked at number 37 in the United States, making it the third highest-charting single on the album.