I have a boids flocking simulation setup. It originally worked by having every boid loop through every boid so that they all constantly know where each other are at in order to tell if they are close or far away, but then I switched to a quadtree design so that boids only have to loop through boids that are actually nearby. However, it has made virtually no improvement to the simulation's FPS. It's as if I'm still looping through every single boid.
Is there some mistake in my implementation? Repo is here, relevant code is mostly in main.js, quadtree.js, and boid.js. LIve site is here
The reason why you are not seeing obvious performance gains from the Quadtree is because of the nature of your simulation. Currently, the default separation causes a large number of boids to "converge" to the same position.
Lots of objects in the same position is going to negate possible speedups due to spatial partitioning. If all the objects are in the same or near position, boids in the area a forced to check against all other boids in the area.
You can demonstrate to yourself that your Quadtree is working by watching or profiling your application with its default settings. Now turn separation up to maximum. You will see visually, or through profiling, that as the boids spread out more evenly, the FPS increases significantly. This is because the Quadtree can now prevent computations thanks to its spatial partitioning.
With default low separation:
With maximum separation:
You can see how performance is increased in the second image. Also note, that the conjecture by another commentor that it is the construction of the Quadtree (insert) that is taking up all the time is wrong.
While in some applications you may be able to update a Quadtree as things move around, since in this simulation every constituent moves every frame, reconstructing the Quadtree from scratch is less work, then taking every object out and reinserting it into its new position.
The advice to skip square-rooting and just use the distance-squared is good though as this will get you a bit more performance.
i need a good pitch shift solution for my project to change the voice. a lot of pitch shift js libraries around - tried them all but they don't provide the desired result. main thing is no control on the result voice timbre and i get Mickey mouse or hell zombie sounding stuff but not real voices with it. while here the result is just outstanding if to test with vega's voice: http://www.sonicapi.com/docs/live-task-demo?task=process-elastiqueTune#demo_form
unfortunately i'm total zero with audio processing and wanna know at least how it's done, what a type of shifting algorythm is used here and how we can achieve timbre/formant control over the process. any hints highly appreciated. thanks ;)
This question touches a very broad subject. Here's a few pointers.
Pitch can be, in general, shifted by offsetting the frequencies that form the voice material. An easy version of this is resampling in temporal domain, where in essential the recording is played back in a different speed. This naturally leads to a tempo change as well which is often not desirable.
In order to preserve the tempo, you need to "explode" the material into its components, in other words, make a domain change from temporal domain to frequency domain. This is what Fourier Transform is for. Once done, you have an estimate of set of frequencies (and respective phases if properly done in complex space) per sample.
The perceived timbre of the voice depends on the relative amplitudes of the frequency set called overtones. Overtones are formed in the speaker's vocal tract and to the listener, heard together with the fundamental frequency. You can control the timbre using different filters in either time domain, spectral
(frequency) domain or cepstral domain. This kind of signal processing is a subject for a library section full of books.
You can move from back from the spectral (frequency) domain to the temporal (time) domain using inverse Fourier transform.
To sum up, the naive approach to shift the pitch you need to transform the samples from temporal to spectral domain, resample along the time axis, and then do the inverse Fourier transform to get back to the time domain.
Besides Fourier transform, you could use wavelets. I hope this gets you started.
I want, for example: to animate how a bird is flying through the sky, i am not talking about the animation of the wings, but how to animate an object random over a canvas.
I use to animate those kind of objects with a lot of randomisation, for example: move [object] from a to b (random distance, random speed, random x, random y, etc.).
But is that really the best way to do it? or is there any algorithm theory available on how to achieve the most natural behaviour.
The animation can be a bird, but it could also be dust, or flying sand)
(I hope my question is clear enough)
In fact, a bird is rarely on its own in the sky, maybe you could have a look at flocking behavior of boids.
They basically lie on 3 rules known as
separation: steer to avoid crowding local flockmates alignment:
steer towards the average heading of local flockmates cohesion:
steer to move toward the average position (center of mass) of local
flockmate
This said you can imagine it is a bit different from sand and dust movement calculation, because these are actual physics problem (mostly fluid mechanics navier stokes)
But I'm pretty sure if you don't actually want to be accurate (navier stokes aren't accurate) you could hack some of boids rules to move particles.
Answer extracted from this post
Lévy flights or brownian motion should work. These are random walks
where at each time step the insect moves a random direction and
distance. They differ in what distribution the random variables are
sampled from.
The motion of hunting sharks can be modeled as brownian motion when
prey is plentiful and lévy flight when prey is scarce.
Depending on what you use it for, you might want to restrict their
motion (to keep them near a specific part of a level) or limit the
acceleration (to make them appear to have have more inertia).
I was looking into implementing an Inertial Navigation System for an Android phone, which I realise is hard given the accelerometer accuracy, and constant fluctuation of readings.
To start with, I set the phone on a flat surface and sampled 1000 accelerometer readings in the X and Y directions (parallel to the table, so no gravity acting in these directions). I then averaged these readings and used this value to calibrate the phone (subtracting this value from each subsequent reading).
I then tested the system by again placing it on the table and sampling 5000 accelerometer readings in the X and Y directions. I would expect, given the calibration, that these accelerations should add up to 0 (roughly) in each direction. However, this is not the case, and the total acceleration over 5000 iterations is nowhere near 0 (averaging around 10 on each axis).
I realise without seeing my code this might be difficult to answer but in a more general sense...
Is this simply an example of how inaccurate the accelerometer readings are on a mobile phone (HTC Desire S), or is it more likely that I've made some errors in my coding?
You get position by integrating the linear acceleration twice but the error is horrible. It is useless in practice.
Here is an explanation why (Google Tech Talk) at 23:20. I highly recommend this video.
It is not the accelerometer noise that causes the problem but the gyro white noise, see subsection 6.2.3 Propagation of Errors. (By the way, you will need the gyroscopes too.)
As for indoor positioning, I have found these useful:
RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers
Pedestrian Tracking with Shoe-Mounted Inertial Sensors
Enhancing the Performance of Pedometers Using a Single Accelerometer
I have no idea how these methods would perform in real-life applications or how to turn them into a nice Android app.
A similar question is this.
UPDATE:
Apparently there is a newer version than the above Oliver J. Woodman, "An introduction to inertial navigation", his PhD thesis:
Pedestrian Localisation for Indoor Environments
I am just thinking out loud, and I haven't played with an android accelerometer API yet, so bear with me.
First of all, traditionally, to get navigation from accelerometers you would need a 6-axis accelerometer. You need accelerations in X, Y, and Z, but also rotations Xr, Yr, and Zr. Without the rotation data, you don't have enough data to establish a vector unless you assume the device never changes it's attitude, which would be pretty limiting. No one reads the TOS anyway.
Oh, and you know that INS drifts with the rotation of the earth, right? So there's that too. One hour later and you're mysteriously climbing on a 15° slope into space. That's assuming you had an INS capable of maintaining location that long, which a phone can't do yet.
A better way to utilize accelerometers -even with a 3-axis accelerometer- for navigation would be to tie into GPS to calibrate the INS whenever possible. Where GPS falls short, INS compliments nicely. GPS can suddenly shoot you off 3 blocks away because you got too close to a tree. INS isn't great, but at least it knows you weren't hit by a meteor.
What you could do is log the phones accelerometer data, and a lot of it. Like weeks worth. Compare it with good (I mean really good) GPS data and use datamining to establish correlation of trends between accelerometer data and known GPS data. (Pro tip: You'll want to check the GPS almanac for days with good geometry and a lot of satellites. Some days you may only have 4 satellites and that's not enough) What you might be able to do is find that when a person is walking with their phone in their pocket, the accelerometer data logs a very specific pattern. Based on the datamining, you establish a profile for that device, with that user, and what sort of velocity that pattern represents when it had GPS data to go along with it. You should be able to detect turns, climbing stairs, sitting down (calibration to 0 velocity time!) and various other tasks. How the phone is being held would need to be treated as separate data inputs entirely. I smell a neural network being used to do the data mining. Something blind to what the inputs mean, in other words. The algorithm would only look for trends in the patterns, and not really paying attention to the actual measurements of the INS. All it would know is historically, when this pattern occurs, the device is traveling and 2.72 m/s X, 0.17m/s Y, 0.01m/s Z, so the device must be doing that now. And it would move the piece forward accordingly. It's important that it's completely blind, because just putting a phone in your pocket might be oriented in one of 4 different orientations, and 8 if you switch pockets. And there's many ways to hold your phone, as well. We're talking a lot of data here.
You'll obviously still have a lot of drift, but I think you'd have better luck this way because the device will know when you stopped walking, and the positional drift will not be a perpetuating. It knows that you're standing still based on historical data. Traditional INS systems don't have this feature. The drift perpetuates to all future measurements and compounds exponentially. Ungodly accuracy, or having a secondary navigation to check with at regular intervals, is absolutely vital with traditional INS.
Each device, and each person would have to have their own profile. It's a lot of data and a lot of calculations. Everyone walks different speeds, with different steps, and puts their phones in different pockets, etc. Surely to implement this in the real world would require number-crunching to be handled server-side.
If you did use GPS for the initial baseline, part of the problem there is GPS tends to have it's own migrations over time, but they are non-perpetuating errors. Sit a receiver in one location and log the data. If there's no WAAS corrections, you can easily get location fixes drifting in random directions 100 feet around you. With WAAS, maybe down to 6 feet. You might actually have better luck with a sub-meter RTK system on a backpack to at least get the ANN's algorithm down.
You will still have angular drift with the INS using my method. This is a problem. But, if you went so far to build an ANN to pour over weeks worth of GPS and INS data among n users, and actually got it working to this point, you obviously don't mind big data so far. Keep going down that path and use more data to help resolve the angular drift: People are creatures of habit. We pretty much do the same things like walk on sidewalks, through doors, up stairs, and don't do crazy things like walk across freeways, through walls, or off balconies.
So let's say you are taking a page from Big Brother and start storing data on where people are going. You can start mapping where people would be expected to walk. It's a pretty sure bet that if the user starts walking up stairs, she's at the same base of stairs that the person before her walked up. After 1000 iterations and some least-squares adjustments, your database pretty much knows where those stairs are with great accuracy. Now you can correct angular drift and location as the person starts walking. When she hits those stairs, or turns down that hall, or travels down a sidewalk, any drift can be corrected. Your database would contain sectors that are weighted by the likelihood that a person would walk there, or that this user has walked there in the past. Spatial databases are optimized for this using divide and conquer to only allocate sectors that are meaningful. It would be sort of like those MIT projects where the laser-equipped robot starts off with a black image, and paints the maze in memory by taking every turn, illuminating where all the walls are.
Areas of high traffic would get higher weights, and areas where no one has ever been get 0 weight. Higher traffic areas are have higher resolution. You would essentially end up with a map of everywhere anyone has been and use it as a prediction model.
I wouldn't be surprised if you could determine what seat a person took in a theater using this method. Given enough users going to the theater, and enough resolution, you would have data mapping each row of the theater, and how wide each row is. The more people visit a location, the higher fidelity with which you could predict that that person is located.
Also, I highly recommend you get a (free) subscription to GPS World magazine if you're interested in the current research into this sort of stuff. Every month I geek out with it.
I'm not sure how great your offset is, because you forgot to include units. ("Around 10 on each axis" doesn't say much. :P) That said, it's still likely due to inaccuracy in the hardware.
The accelerometer is fine for things like determining the phone's orientation relative to gravity, or detecting gestures (shaking or bumping the phone, etc.)
However, trying to do dead reckoning using the accelerometer is going to subject you to a lot of compound error. The accelerometer would need to be insanely accurate otherwise, and this isn't a common use case, so I doubt hardware manufacturers are optimizing for it.
Android accelerometer is digital, it samples acceleration using the same number of "buckets", lets say there are 256 buckets and the accelerometer is capable of sensing from -2g to +2g. This means that your output would be quantized in terms of these "buckets" and would be jumping around some set of values.
To calibrate an android accelerometer, you need to sample a lot more than 1000 points and find the "mode" around which the accelerometer is fluctuating. Then find the number of digital points by how much the output fluctuates and use that for your filtering.
I recommend Kalman filtering once you get the mode and +/- fluctuation.
I realise this is quite old, but the issue at hand is not addressed in ANY of the answers given.
What you are seeing is the linear acceleration of the device including the effect of gravity. If you lay the phone on a flat surface the sensor will report the acceleration due to gravity which is approximately 9.80665 m/s2, hence giving the 10 you are seeing. The sensors are inaccurate, but they are not THAT inaccurate! See here for some useful links and information about the sensor you may be after.
You are making the assumption that the accelerometer readings in the X and Y directions, which in this case is entirely hardware noise, would form a normal distribution around your average. Apparently that is not the case.
One thing you can try is to plot these values on a graph and see whether any pattern emerges. If not then the noise is statistically random and cannot be calibrated against--at least for your particular phone hardware.
I'm doing voice recording in javascript, and storing the recording as an array of signed floats. What would I need to determine (and ultimately, adjust) pitch on the array? I've seen various algorithms for C++, but they don't seem to be very helpful in my situation. I even downloaded and tried this one to see if I could convert parts of it to javascript:
http://voicerecorder.codeplex.com/SourceControl/latest
But all that actually did was make the recording louder, regardless of the settings I chose.
I'm not going to try to provide an exhaustive answer here, but rather describe my own findings discovered on my journey of wrestling with similar issues in audio programming.
Pitch Detection
If you're sound is monophonic (as it sounds that is is based on your comment to jeff), I've implemented pitch detection using auto-correlation techniques, mostly because it's relatively simple compared to other pitch detection algorithms.
The idea, if you're unfamiliar, is as follows:
Slide a sample over itself (with a predetermined window size; in 1-sample increments)
At each step, calculate the absolute difference between the original wave and the slid window (hard to explain verbally).
as you slide the window, keep a record of the score calculated in (2)
when the wave correlates with itself, the score will hit a minimum, and the time-location of this minimum specifies the signals periodicity.
In my implementation, this was the only algorithm that worked well (when fed samples of my voice; I didn't try a variety of samples however).
That was a crude explanation of how autocorrelation works, and this article provides a very nice comparison of different pitch-detection algorithms:
https://ccrma.stanford.edu/~pdelac/154/m154paper.htm
Pitch Shifting
Of course, you could get really cheap pitch shifting by just resampling, but that sounds similar to a record being played too fast, which is not acceptable in many circumstances.
As far as pitch shifting goes, I haven't gotten that far yet in my implementation, but last I left off, I was looking at phase vocoders as a possible solution. What's hard is finding a decent explanation of how these algorithms work that provides some intuition on the reason why they work the way they do instead of just providing soley abstract mathmatical equations.