if I have a sound file in .dat format, that looks like this
; Sample Rate 22050
0.0 -0.0390625
4.5351473922902495E-5 -0.0390625
9.070294784580499E-5 -0.0390625
1.3605442176870748E-4 -0.0390625
Where the left column is the second and the right column is the amplitude (-1.0 to 1.0)
How do I double the frequency of the sound? Could someone give me the big picture of it?
My understanding is that the frequency is how many times the sound is repeated over second. So if I halve all the value in the second column, does that double the frequency?
Thank you
To double the frequency, you want to halve every value in the left column. This will make any given part of the sound take half as much time, which is the same as saying the frequency is doubled.
Use the same data and double the sample rate, or use the same samplerate and remove every other sample.
Any method you use would result in the sound only last for half as long. A dirty trick for working around this is to repeat a set amount of samples to make up for it.
Related
I have some data that I'm using to plot a curve in excel. It uses a non-linear calculation.
The calculation is called the rule of twelfths - it is used to calculate changes in tidal height between a high and low tide. The rule states that in the first sixth (often approximated to an hour) of the time period, the tide will move 1/12th of the overall range. In the second sixth, the tide will move 2/12th's of the overall range. In the 3rd and 4th sixth, the tide will move 3/12ths (in each), and then it will move 2/12ths again in the fifth sixth, and 1/12th in the final sixth.
The maths for this is relatively straightforward - if I know the High Water Time and Low Water time, and their respective heights, I can calculate a data point for each sixth. That then plots to a nice even curve (and some fun pie chart shenanigans shows it on a clock face too).
This produces the following sheet:
What I am now after is the ability to overlay onto that the height for a given time of day. This would be used in a 'live' sense to display the height 'now', or perhaps where the user dragged their finger on the curve if it was in an app. I'm only using this for screenshot/flat file purposes, so I just need it to base the overlay one the data in one cell.
So, in the attached screenshot, if we had a time of day of 1128, (based on Cell J3), excel would take the time in J3, and wherever it intersected the curve, draw both a vertical and a horizontal line, so that the height of tide data (HOT) could be measured off that axis.
This would look something like this (I've circled cell J3 too):
Is that something that's possible? It might be that it needs to do a lookup in the table of calculated data points and then interpolate just between those two - that would probably get close enough.
A two stage question I guess - firstly calculating the intercept, secondly getting it to draw on (complete with the vertical and horizontal lines if possible!).
There's a widget on planetcalc which does almost the same thing - it only gives the calculated data points (and it uses hours rather than the range), but it gives a nice visual idea.
PlanetCalc Tide Calculator
Any thoughts? Is it possible?
I created a solution to this myself in the end.
Given that the two values were easily calculated, I produced a pair of values for the desired time/height, and plotted each as an additional line graph on top of the existing (styled correctly).
This gave the appearance of what I wanted, and intersected my curve perfectly.
In my study I calculate some ratios. The theoretical background is as follows:
There is the effect of Binocular Rivalry, where a different picture is presented to the left eye than to the right eye (e.g. a black and a white square). Most of the time, the test persons do not see a mixture of colours (i.e. something grey), but the picture changes back and forth, so a black square is seen once and then a white one. During the time of the trial (e.g. 60 seconds) the test persons indicate what they see (black square, white square, mixed picture). These durations can be used to calculate the predominance ratio as an indication of whether one stimulus is seen significantly more often than the other. The ratio is calculated from [T(stimulus1)-T(stimulus2)/T(stimulus1)+T(stimulus2)], where T is the cumulative time the stimulus was seen during the 60 seconds. The times for the mixed image are completely omitted from this calculation. In the end the ratio is tested if it is significantly different from zero with a one-sample t-test. If it is significantly different from zero and positive, stimulus 1 is seen longer, if it is significantly different from zero and negative, stimulus 2 is seen longer. Now I have two conditions and I calculate a predominance ratio for each.
Let us suppose that condition 1 would be the squares I mentioned above and condition 2 would be a stick figure in black and a tree in white. I want to know if there is a significant predominance ratio in the stickman/tree condition, but without the influence of the colors. Therefore I want to somehow deduct the predominance ratio from condition 1 from condition 2. So I would like to do a kind of "baseline correction". The value of this predominance ratio can vary between -1 and 1. Now my question is how to do this correction without changing the metrics of the ratio. In order to test the corrected ratio towards zero in a meaningful way, it must not take any other values than from -1 to 1.
Does anyone have an idea?
Thanks a lot!
I'm trying to determine where, in a set of measurement data, the data takes a dive...
... so I can plot a vertical line and
... plot a horizontal line in the graph.
I have no problem doing the 2nd and 3rd bullet points above on my own, so that's taken care of.
The problem I need help with is the first bullet point - determining WHERE the data takes a dive - WHERE the data crosses a threshold that basically says, "Whatever-it-is you're measuring, is no longer performing as it is expected to.".
Here's what I'm doing:
I am taking measurements using a measuring device and that device is logging the measurements in its internal memory and allowing me to download that measurement data to my computer into a csv when the test session is complete.
I pull that csv into an xls and plot the data on a graph. (see attached image)
Here's what I want to do:
If you look at the attached image I would like to find the value where the data DEFINITELY crosses BELOW the horizontal line so I can say, "Here is where the device being tested 'gave up the ghost' and was no longer able to perform as desired."
What the data roughly looks like:
Each measurement set will have the rough look and feel of the attached image but slightly different each time. (because each object I am testing will have roughly the same performance characteristics but they all have their own manufacturing defects and variations.)
The data set for the attached image is a data set of 7000 measurements.
I never really know where the horizontal line will be.
Examples of the data sets I have gotten in the past several tests look like this:
(394 to 0)
(390000 to 0)
(3.88 to 0)
(375000 to 0)
(39.55 to 0)
(59200 to 0)
and each data set will have about 1,000 to 7,000 measurements each.
Here's how I was trying to solve this issue:
I was using SLOPE() and trying to latch onto where the slop of the line took a dive / started to work its way to a zero slope (which is a vertical line) so when it starts approaching a really small slope then it MUST be taking a dive. That didn't really work.
I was looking at using STDEV.P() in Excel and feeding it the entire data set. Then I was looking at doing the same thing but feeding it only the first 10, 30, 60 measurements but then I thought - we never really know just how many measurements will come through. Then I thought I would use the first 10% of the measurements and feed that to STDEV.P().
Please let me know what you think of this and please let me know of any ideas you may have.
Thanks.
H
Something like this should work to flag when the decay rate increases.
To find what 'direction' your data is going in you need the derivative.
Excel doesn't have a derivative formula but you can set it up pretty easily by using the (change in y)/(change in x) as demonstrated here:
http://faculty.educ.ubc.ca/sanderson/lab/CLFbiom/demo/diff.htm
I would then check a formula which counts how many datarows you have (=COUNTA(A:A) or similar)
Then uses that to get a step of 10% of your data
Then check the value of the derivative in a cell against a cell 10% further down. If it's still a negative (to account for the slight downhill at first) then you'll know
The right way to go about this is to model the data with an unknown discontinuity, something like "if time < break_time then (some constant plus noise) else (decaying exponential)". A maximum likelihood estimation for that model might require iteration or other operations which are clumsy in Excel -- maybe you should consider VB or Python or some other programming language. I.e. choose the tool to fit the problem and not the other way around.
See Seber and Wild, "Nonlinear Regression", for an extensive discussion of models with discontinuities.
If your data can be generally characterized as having:
(A) a more or less flat plateau region, followed by
(B) a downward trending region
then a basic strategy could be to start at then end of the data and march towards the beginning one point at a time, checking to see that the values are increasing. Once they stop increasing, you've found the break point.
The strategy assumes (unwisely?) that the downward trending region is smooth/noiseless. To make the solution more robust to noise, you could compare values that are 5 apart, or 10 apart, or whatever interval works to filter out the noise. Or you could use a moving average.
This strategy could potentially be made more efficient by starting the search somewhere in the middle of the data but still in downward trending portion. If you know (based on experience) that any value that is (say) 0.5X the maximum is in the downward trending portion, you could start the search there.
Hope that helps.
It appears as though you want to detect when the slope changes from something near zero to something negative. One way to detect this is to calculate the 2nd derivative of the values (calculate the slope of the slope). The 2nd derivative should be near zero in the flat portion of the data AND in the downward trending portion of the data. It should go negative at the break point. So finding the minimum (most negative) value of the 2nd should locate the break point.
To implement this, you probably will need to filter noise. So calculate the first derivative (slope) over some suitable window of data:
=SLOPE(moving window of say 25 raw values)
Then calculate the second derivative (slope of slope):
=SLOPE(moving window of say 25 slope values)
Then look for the minimum.
Hope that helps.
I have several curves that contain many data points. The x-axis is time and let's say I have n curves with data points corresponding to times on the x-axis.
Is there a way to get an "average" of the n curves, despite the fact that the data points are located at different x-points?
I was thinking maybe something like using a histogram to bin the values, but I am not sure which code to start with that could accomplish something like this.
Can Excel or MATLAB do this?
I would also like to plot the standard deviation of the averaged curve.
One concern is: The distribution amongst the x-values is not uniform. There are many more values closer to t=0, but at t=5 (for example), the frequency of data points is much less.
Another concern. What happens if two values fall within 1 bin? I assume I would need the average of these values before calculating the averaged curve.
I hope this conveys what I would like to do.
Any ideas on what code I could use (MATLAB, EXCEL etc) to accomplish my goal?
Since your series' are not uniformly distributed, interpolating prior to computing the mean is one way to avoid biasing towards times where you have more frequent samples. Note that by definition, interpolation will likely reduce the range of your values, i.e. the interpolated points aren't likely to fall exactly at the times of your measured points. This has a greater effect on the extreme statistics (e.g. 5th and 95th percentiles) rather than the mean. If you plan on going this route, you'll need the interp1 and mean functions
An alternative is to do a weighted mean. This way you avoid truncating the range of your measured values. Assuming x is a vector of measured values and t is a vector of measurement times in seconds from some reference time then you can compute the weighted mean by:
timeStep = diff(t);
weightedMean = timeStep .* x(1:end-1) / sum(timeStep);
As mentioned in the comments above, a sample of your data would help a lot in suggesting the appropriate method for calculating the "average".
I have a data set that has height values every so often, like topography data in a straight line with GPS coordinates. I used the GPS coordinates and trigonometry to make a cumulative distance column. However, the distance between points varies. Sometimes its 10 cm sometimes its 13, sometimes its 40.
I would like to take the average height every 0.5 meters, but sometimes the distance column doesnt even land on a multiple of 0.5! This would mean my output column would be significantly shorter than my raw data column.
I think my main problem is I do not know what this process is called in order to Google it. Another problem is that the distances are irregular as mentioned above. Things I think may have something to do with it:
averageif?
binning? I do not want a histrogram though, just the data.
Thanks for the help and if you do not know the answer but at least know what I should be writing in the search bars that would be helpful as well. Thanks!
Perhaps this will work for you. I made up a series of distance vs height measurements and determined that a third order polynomial curve fit pretty well. (A different curve might best fit your real data, so you would have to alter the formula accordingly). I then used that formula to derive a set of new heights for the desired ditances at, in my example five unit differences.
The formula under Extrapolated heights is an ARRAY formula entered into all the cells at once. You select D2:D12, enter the formula in D2 and, hold down CTRL-SHIFT while hitting ENTER. If you did this correctly, you will have the same formula in each cell surrounded by curly braces {...}
Then you can decide how you want to "Average" the heights.