Hi: I want to do a sound waves simulation that include wave propagation, absorbing and reflection in 3D space.
I do some searches and I found this question in stackoverflow but it talk about electromagnetic waves not sound waves.
I know i can reimplement the FDTD method for sound waves but how about the sources and does it act like the electromagnetic waves ? Is there any resources to start with ?
Thanks in advance.
Hope this can give you some inputs...
As far as i know, in EM simulations obstacles (and thus terrain) are not considered at all. With sound you have to consider reflection, diffraction, etc
there are different standards to calculate the noise originated from different sources (I'll list the europe ones, the one i know of):
traffic, NMPB (NMPB-Routes-96) is THE standard. All the noise calculations have to be done with that one (at least in my country). Results aren't very good. A "new" algorithm is SonRoad (i think it uses inverse ray-tracing)... from my tests it works great.
trains: Schall03
industries, ISO 9613
a list of all the used models in CadnaA (a professional software) so you can google them all: http://www.datakustik.com/en/products/cadnaa/modeling-and-calculation/calculation-standards/
another pro software is SoundPlan, somewhere on the web there is a free "SoundPlan-ReferenceManual.pdf" 800-pages with the mathematical description of the implemented algorithms... i haven't had any luck with google today tough
An easy way to do this is use the SoundPlan software. Multiple sound propagation methods such as ISO9613-2, CONCAWE and Nord2000 are implemented. It has basic 3D visualization with sound pressure level contours.
Related
I am taking part in a programming competition where the objective is writing a bot that can play a specific game.
The objective of the game is to earn a certain amount of points. You control multiple airships, that you move around, capture islands and navigate drones that carry treasure. You play against one opponent, turns happen simultaneously, and there is a time limit. You can move multiple ships and drones in one turn. You can program your bot in Python, Java or C#.
The exact details don‘t matter, just that each ship has around 15 options each turn (moving and shooting) and overall you have around 10000 different options for each turn (different configurations of airship movements and shooting)
Up until now I approached this competition naively, and haven‘t done anything exceptionally clever (for example, if near enemy, shoot). I have read about minimax algorithms, and I would really like to apply it here (or something similar), you can assume that I can tell the value of a state. My problem is the mass of options for each turn - which create an enourmous branching factor that doesnt let me get very deep.
Question 1: Is there a better, applicable approach to this problem? Perhaps deep-learning or something similar?
Question 2: Is there a way to minimize the branching factor? I`ve read about alpha-beta and similar algorithms, but nothing seems to do the job.
Any help would be much appreciated
The minimax algorithm seems to be natural for these kinds of problems. At first, the game will be modelled in a abstract way and then a solver is used to find the path from current situation to a gamestate which maximize the amount of points. A similar approach to minimax is GOAP, which was implemented in the 1970'er for Shakey the robot under the name STRIPS. But, GOAP and minimax has two problems: first, a abstract model of the game is needed (perhaps in PDDL or in Game Description Language) and second the state-space is to big.
An better alternative to planning is to use a Behavior Tree. Thats a static program which describes the behavior of an agent. No solver is needed and no complete modelling of the game is needed. Instead, a bottom up approach is used with multiple edit-compile-run iterations for finding the optimal behavior tree (Test-driven-development). To implement such programming approach a so called "reactive planner" has to be implemented first which is another word for a realtime scheduler. Thats a module whichs maps a behavior tree onto a gantt-chart for executing an action at a specific moment in time. As introduction, the unity3d Engine is a good starting point, which has a full behaviortree implementation out-of-the-box.
I just came to know about this excellent tutorials
http://scientificgems.wordpress.com/2013/12/11/integrating-netlogo-and-java-part-1/
http://scientificgems.wordpress.com/2013/12/12/integrating-netlogo-and-java-2/
http://scientificgems.wordpress.com/2013/12/13/integrating-netlogo-and-java-3/
Their example concerns about computation needed for patch diffusion and shows how to access patch variable from java and change them in netlogo.
I was wondering if anyone has any idea or comments on when we should think of writing an extension to make our model work better? I am new to netlogo itself, but I think it's good to know what are the options that I might not be aware of :)
I think looking through the extensions listed at https://github.com/NetLogo/NetLogo/wiki/Extensions , both the ones that we (on the NetLogo team) have built ourselves and the ones that have come from the user community, gives you a pretty good idea of the range of things extensions can be good for.
Some broad categories:
data structures (tables, arrays, matrices, priority queues...)
algorithms (networks, statistics, discrete event scheduling, diffusion, ...)
integration with other tools (R, SQL databases, MatLab, ...)
media (sound playback, sound synthesis, images, movies, speech, ...)
new device types (Gogo, Arduino, WiiMote...)
visualization (ray-tracing, sprites, Java2D drawing, ...)
not necessarily exhaustive!
Yes, I'm aware that speech recognition is fairly complicated (as an understatement). What I'm looking for is a method for distinguishing between maybe 20-30 phrases. An ability to split words (discrete speech is fine) would be nice, but isn't required. The software will be user-dependent(i.e. for use by me). I'm not looking for existing software, but for a good way of going about doing this myself. I've looked into various existing methods and it seems like splitting the sound into phonemes, while common, is somewhat excessive for my needs.
For some context, I'm just looking for a way to control some aspects of my computer with a few simple voice commands. I'm aware that Windows already has speech recognition software, but I'd like to go about this one myself as a learning exercise. Commands would be simple like "Open Google", or "Mute". What I had in mind (not sure if this is a good idea) is that some commands would be compound. So "Mute" would just be "Mute". Whereas the "Open" command could be recognized individually, and then have its suffixes (Google, Photoshop, etc). recognized with another network/model/whatever. But I'm not sure if looking for prefixes/word breaks in this way would produce better results than having to deal with an increased number of individual commands.
I've been looking into perceptrons, hopfield networks (though they're somewhat obsolete from what I understand) and HMMs, and while I understand the ideas behind these (I've implemented the ANNs before) I don't really know which is best suited to this task. I'm assuming that linear vector quantization models would also be appropriate, but I can't really find much literature to this end. Any guidance/resources would be greatly appreciated.
There are some open source project in speech recognition:
HTK (Hidden Markov Models Toolkit)
Sphinx
Both have decoder, training, language model toolkits. Eveything to build a complete and robust speech recognizer.
Voxforge has acoustic and language models for both open source speech recognition toolkits.
Some time ago, I read a whitepaper about a limited vocabulary system, which used a simple recognition process. The system divided each utterance into a small number of bins (6 in time, and 4 in magnitude, if I remember correctly, for 24 total), and all it did was count the number of sample audio measurements in each bin. There was a fuzzy logic rule base which then interpreted each utterances 24 bin counts, and generated an interpretation.
I imagine that (for some applications) a simple matching process might work just as well, in which the 24 bin counts of the current utterance are simple matched against those of each of your stored prototypes, and the one with the least overall difference is the winner.
Yamaha InfoSound and ShopKick application use technologies that allow to transfer data using ultrasound. That is playing an inaudible signal (>18kHz) that can be picked up by modern mobile phones (iOS, Android).
What is the approach used in such technologies? What kind of modulation they use?
I see several problems with this approach. First, 18kHz is not inaudible. Many people cannot hear it, especially as they age, but I know I certainly can (I do regular hearing tests, work-related). Also, most phones have different low-pass filters on their A/D converters, and many devices, especially older Android ones (I've personally seen that happen), filter everything below 16 kHz or so. Your app therefore is not guaranteed to work on any hardware. The iPhone should probably be able to do it.
In terms of modulation, it could be anything really, but I would definitely rule out AM. Sound has next to zero robustness when it comes to volume. If I were to implement something like that, I would go with FSK. I would think that PSK would fail due to acoustic reflections and such. The difficulty is that you're working with non-robust energy transfer within a very narrow bandwidth. I certainly do not doubt that it can be achieved, but I don't see something like this proving reliable. Just IMHO, that is.
Update: Now that i think about it, a plain on-off would work with a single tone if you're not transferring any data, just some short signals.
Can't say for Yamaha InfoSound and ShopKick, but what we used in our project was a variation of frequency modulation: the frequency of the carrier is modulated by a digital binary signal, where 0 and 1 correspond to 17 kHz and 18 kHz respectively. As for demodulator, we tried heterodyne. More details you could find here: http://rnd.azoft.com/mobile-app-transering-data-using-ultrasound/
There's nothing special in being ultrasound, the principle is the same as data transmission through a modem, so any digital modulation is -in principle- feasible. You only have a specific frequency band (above 18khz) and some practical requisites (the medium is very unreliable, I guess) that suggest to use a simple-robust scheme with low-bit rate.
I don't know how they do it but this is how I do it:
If it is a string then make sure it's not a long one (the longer the higher is the error probability ). Lets assume we're working with the vital part of the ASCII code, namely up to character number 127, then all you need is 7 bits per character. Transform this character into bits and modulate those bits using QFSK (there are several modulations to choose from, frequency shift based ones have turned out to be the most robust I've tried from the conventional ones... I've created my own modulation scheme for this use case). Select the carrier frequencies as 18.5,19,19.5, and 20 kHz (if you want to be mathematically strict in your design, select frequency values that assure you both orthogonality and phase continuity at symbol transitions, if you can't, a good workaround to avoid abrupt symbols transitions is to multiply your symbols by a window of the same size, eg. a Gaussian or Bartlet ). In my experience you can move this values in the range from 17.5 to 20.5 kHz (if you go lower it will start to bother people using your app, if you go higher the average type microphone frequency response will attenuate your transmission and induce unwanted errors).
On the receiver side implement a correlation or matched filter receiver (an FFT receiver works as well, specially a zero padded one but it might be a little bit slower, I wouldn't recommend Goertzel because frequency shift due to Doppler effect or speaker-microphone non-linearities could affect your reception). Once you have received the bit stream make characters with them and you will recover your message
If you face too many broadcasting errors, try selecting a higher amount of samples per symbol or band-pass filtering each frequency value before giving them to the demodulator, using an error correction code such as BCH or Reed Solomon is sometimes the only way to assure an error free communication.
One topic everybody always forgets to talk about is synchronization (to know on the receiver side when the transmission has begun), you have to be creative here and make a lot a tests with a lot of phones before you can derive an actual detection threshold that works on all, notice that this might also be distance dependent
If you are unfamiliar with these subjects I would recommend a couple of great books:
Digital Modulation Techniques from Fuqin Xiong
DIGITAL COMMUNICATIONS Fundamentals and Applications from BERNARD SKLAR
Digital Communications from John G. Proakis
You might have luck with a library I created for sound-based modems, libquiet. It gives you a handful of profiles to work from, including a slow "Ultrasonic whisper" profile with spectral content above 19kHz. The library is written in C but would require some work to interface with iOS.
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So how does someone create a music visualizer? I've looked on Google but I haven't really found anything that talks about the actual programming; mostly just links to plug-ins or visualizing applications.
I use iTunes but I realize that I need Xcode to program for that (I'm currently deployed in Iraq and can't download that large of a file). So right now I'm just interested in learning "the theory" behind it, like processing the frequencies and whatever else is required.
As a visualizer plays a song file, it reads the audio data in very short time slices (usually less than 20 milliseconds). The visualizer does a Fourier transform on each slice, extracting the frequency components, and updates the visual display using the frequency information.
How the visual display is updated in response to the frequency info is up to the programmer. Generally, the graphics methods have to be extremely fast and lightweight in order to update the visuals in time with the music (and not bog down the PC). In the early days (and still), visualizers often modified the color palette in Windows directly to achieve some pretty cool effects.
One characteristic of frequency-component-based visualizers is that they don't often seem to respond to the "beats" of music (like percussion hits, for example) very well. More interesting and responsive visualizers can be written that combine the frequency-domain information with an awareness of "spikes" in the audio that often correspond to percussion hits.
For creating BeatHarness ( http://www.beatharness.com ) I've 'simply' used an FFT to get the audiospectrum, then use some filtering and edge / onset-detectors.
About the Fast Fourier Transform :
http://en.wikipedia.org/wiki/Fast_Fourier_transform
If you're accustomed to math you might want to read Paul Bourke's page :
http://local.wasp.uwa.edu.au/~pbourke/miscellaneous/dft/
(Paul Bourke is a name you want to google anyway, he has a lot of information about topics you either want to know right now or probably in the next 2 years ;))
If you want to read about beat/tempo-detection google for Masataka Goto, he's written some interesting papers about it.
Edit:
His homepage : http://staff.aist.go.jp/m.goto/
Interesting read : http://staff.aist.go.jp/m.goto/PROJ/bts.html
Once you have some values for for example bass, midtones, treble and volume(left and right),
it's all up to your imagination what to do with them.
Display a picture, multiply the size by the bass for example - you'll get a picture that'll zoom in on the beat, etc.
Typically, you take a certain amount of the audio data, run a frequency analysis over it, and use that data to modify some graphic that's being displayed over and over. The obvious way to do the frequency analysis is with an FFT, but simple tone detection can work just as well, with a lower lower computational overhead.
So, for example, you write a routine that continually draws a series of shapes arranged in a circle. You then use the dominant frequencies to determine the color of the circles, and use the volume to set the size.
There are a variety of ways of processing the audio data, the simplest of which is just to display it as a rapidly changing waveform, and then apply some graphical effect to that. Similarly, things like the volume can be calculated (and passed as a parameter to some graphics routine) without doing a Fast Fourier Transform to get frequencies: just calculate the average amplitude of the signal.
Converting the data to the frequency domain using an FFT or otherwise allows more sophisticated effects, including things like spectrograms. It's deceptively tricky though to detect even quite 'obvious' things like the timing of drum beats or the pitch of notes directly from the FFT output
Reliable beat-detection and tone-detection are hard problems, especially in real time. I'm no expert, but this page runs through some simple example algorithms and their results.
Devise an algorithm to draw something interesting on the screen given a set of variables
Devise a way to convert an audio stream into a set of variables analysing things such as beats/minute frequency different frequency ranges, tone etc.
Plug the variables into your algorithm and watch it draw.
A simple visualization would be one that changed the colour of the screen every time the music went over a certain freq threshhold. or to just write the bpm onto the screen. or just displaying an ociliscope.
check out this wikipedia article
Like suggested by #Pragmaticyankee processing is indeed an interesting way to visualize your music. You could load your music in Ableton Live, and use an EQ to filter out the high, middle and low frequencies from your music. You could then use a VST follwoing plugin to convert audio enveloppes into MIDI CC messages, such as Gatefish by Mokafix Audio (works on windows) or PizMidi’s midiAudioToCC plugin (works on mac). You can then send these MIDI CC messages to a light-emitting hardware tool that supports MIDI, for instance percussa audiocubes. You could use a cube for every frequency you want to display, and assign a color to the cube. Have a look at this post:
http://www.percussa.com/2012/08/18/how-do-i-generate-rgb-light-effects-using-audio-signals-featured-question/
We have lately added DirectSound-based audio data input routines in LightningChart data visualization library. LightningChart SDK is set of components for Visual Studio .NET (WPF and WinForms), you may find it useful.
With AudioInput component, you can get real-time waveform data samples from sound device. You can play the sound from any source, like Spotify, WinAmp, CD/DVD player, or use mic-in connector.
With SpectrumCalculator component, you can get power spectrum (FFT conversion) that is handy in many visualizations.
With LightningChartUltimate component you can visualize data in many different forms, like waveform graphs, bar graphs, heatmaps, spectrograms, 3D spectrograms, 3D lines etc. and they can be combined. All rendering takes place through Direct3D acceleration.
Our own examples in the SDK have a scientific approach, not really having much entertainment aspect, but it definitely can be used for awesome entertainment visualizations too.
We have also configurable SignalGenerator (sweeps, multi-channel configurations, sines, squares, triangles, and noise waveforms, WAV real-time streaming, and DirectX audio output components for sending wave data out from speakers or line-output.
[I'm CTO of LightningChart components, doing this stuff just because I like it :-) ]