I'm currently researching an problem regarding DOA (direction of arrival) regression for an audio source, and need to generate training data in the form of audio signals of moving sound sources. In particular, I have the stationary sound files, and I need to simulate a source and microphone(s) with the distances between them changing to reflect movement.
Is there any software online that could potentially do the trick? I've looked into pyroomacoustics and VA as well as other potential libraries, but none of them seem to deal with moving audio sources, due to the difficulties in simulating the doppler effect.
If I were to write up my own simulation code for dealing with this, how difficult would it be? My use case would be an audio source and a microphone in some 2D landscape, both moving with their own velocities, where I would want to collect the recording from the microphone as an audio file.
Some speculation here on my part, as I have only dabbled with writing some aspects of what you are asking about and am not experienced with any particular libraries. Likelihood is good that something exists and will turn up.
That said, I wonder if it would be possible to use either the Unreal or Unity game engine. Both, as far as I can remember, grant the ability to load your own cues and support 3D including Doppler.
As far as writing your own, a lot depends on what you already know. With a single-point mike (as opposed to stereo) the pitch shifting involved is not that hard. There is a technique that involves stepping through the audio file's DSP data using linear interpolation for steps that lie in between the data points, which is considered to have sufficient fidelity for most purposes. Lot's of trig, too, to track the changes in velocity.
If we are dealing with stereo, though, it does get more complicated, depending on how far you want to go with it. The head masks high frequencies, so real time filtering would be needed. Also it would be good to implement delay to match the different arrival times at each ear. And if you start talking about pinnas, I'm way out of my league.
As of now it seems like Pyroomacoustics does not support moving sound sources. However, do check a possible workaround suggested by the developers here in Issue #105 - where the idea of using a time-varying convolution on a dense microphone array is suggested.
I've combed StackOverflow and the web for many questions on whistle detection, etc, and many people did explain as much as they could as to how they can go about detecting their stuff.
capturing sound for analysis and visualizing frequences in android
analyzing whistle sound for pitch note
But what I don't get is how does FFT help you to detect certain sounds in a given sample audio data?
Here's what I understand so far from some stuff I found here and there.
-The sine wave is more or less the building block of ALL signals, musical or not
-Three parameters - FREQUENCY, AMPLITUDE, and INITIAL PHASE, characterize every steady sine wave completely.
-They make each and any kind of wave unique.
-Fourier transform can be used to inspect what kinds of sine waves there are in a signal
SOURCE -- [Audio signal processing basics][3]
Audio data that the computer generates as received from the mic or other input source, for live processing, is an array of amplitudes processed (or stored or taken) at a particular sample rate.
So how does one go from that to detecting whistles and claps?
And complex things such as say, a short period of whistling to a particular song?
My theory of detecting is that we test our whistles in a spectogram, and record the particular frequency and amplitude characteristics. And then if those particular characteristics are repeated again in the input, we've detected a whistle.
Am I right or wrong?
This sound processing stuff is a little complicated.
Forgot to mention this - I'm using Python. Java is also okay, since most of the examplar code I found was for Android which is in Java. And I can work in Java too. Any mention of any libraries or APIs would be helpful too.
Is it possible with FFT to find a drum solo, or a drum break, in an audio file? Is this something FFT is able to do and are there any resources online that could aid me with learning?
In general, a FFT is not a good choice for detecting the onset of percussion sounds:
An FFT is always calculated over a window of samples (in effect a period of time) and yields the magnitude of signal within the bin and its phase offset. You can therefore determine that there is signal at that particular bin, but not its onset time. The best time resolution available is the window period. Of course, you can make the period shorter at the expense of frequency resolution.
Percussion sounds tend to look like noise and spread across the spectrum. This would be OK if you only had percussions sounds, but is not great in real-life polyphonic content.
However, you might be able to find some inference from the different characteristics of the spectra of a drum solo vs instrumental sections of a track.
The problem of finding the time at which percussion sounds start in music is described in academic journals as onset dectection and is one of the many techniques used for feature extraction; the wider field is known as Music Information Retrieval. Your problem sounds like one of identifying sections in audio files and this might be described as partitioning
A good place to start is Sonic Visualiser which is a tool written specifically for MIR applications. Plug-ins exist for various types of feature extraction. From these you will be able to easily find the large body of academic work in this area. There is an added bonus that the existing plug-ins are all open source too.
I'd look here, there was a bit of discussion with great pointers on the Gamedev SE: https://gamedev.stackexchange.com/questions/9761/beat-detection-and-fft :-)
I'm trying to compare sound clips based on microphone recording. Simply put I play an MP3 file while recording from the speakers, then attempt to match the two files. I have the algorithms in place that works, but I'm seeing a slight difference I'd like to sort out to get better accuracy.
The microphone seem to favor some frequencies (add amplitude), and be slightly off on others (peaks are wider on the mic).
I'm wondering what the cause of this difference is, and how to compensate for it.
Background:
Because of speed issues in how I'm doing comparison I select certain frequencies with certain characteristics. The problem is that a high percentage of these (depending on how many I choose) don't match between MP3 and mic.
It's called the response characteristic of the microphone. Unfortunately, you can't easily get around it without buying a different, presumably more expensive, microphone.
If you can measure the actual microphone frequency response by some method (which generally requires having some etalon acoustic system and an anechoic chamber), you can compensate for it by applying an equaliser tuned to exactly inverse characteristic, like discussed here. But in practice, as Kilian says, it's much simpler to get a more precise microphone. I'd recommend a condenser or an electrostatic one.
Despite all the advances in 3D graphic engines, it strikes me as odd that the same level of attention hasn't been given to audio. Modern games do real-time rendering of 3D scenes, yet we still get more-or-less pre-canned audio accompanying those scenes.
Imagine - if you will - a 3D engine that models not just the physical appearance of items, but also their audio properties. And from these models it can dynamically generate audio based on the materials that come into contact, their velocity, distance from your virtual ears, etcetera. Now, when you're crouching behind the sandbags with bullets flying over your head, each one will yield a unique and realistic sound.
The obvious application of such a technology would be gaming, but I'm sure there are many other possibilities.
Is such a technology being actively developed? Does anyone know of any projects that attempt to achieve this?
Thanks,
Kent
I once did some research toward improving OpenAL, and the problem with simulating 3D audio is that so many of the cues that your mind uses — the slightly different attenuation at various angles, the frequency difference between sounds in front of you and those behind you — are quite specific to your own head and are not quite the same for anyone else!
If you want, say, a pair of headphones to really make it sound like a creature is in the leaves ahead and in front of the character in a game, then you actually have to take that player into a studio, measure how their own particular ears and head change the amplitude and phase of the sound at different distances (amplitude and phase are different, and are both quite important to the way your brain processes sound direction), and then teach the game to attenuate and phase-shift the sounds for that particular player.
There do exist "standard heads" that have been mocked up with plastic and used to get generic frequency-response curves for the various directions around the head, but an average or standard will never sound quite right to most players.
Thus the current technology is basically to sell the player five cheap speakers, have them place them around their desk, and then the sounds — while not particularly well reproduced — actually do sound like they're coming from behind or beside the player because, well, they are coming from the speaker behind the player. :-)
But some games do bother to be careful to compute how sound would be muffled and attenuated through walls and doors (which can get difficult to simulate, because the ear receives the same sound at a few milliseconds different delay through various materials and reflective surfaces in the environment, all of which would have to be included if things were to sound realistic). They tend to keep their libraries under wraps, however, so public reference implementations like OpenAL tend to be pretty primitive.
Edit: here is a link to an online data set that I found at the time, that could be used as a starting point for creating a more realistic OpenAL sound field, from MIT:
http://sound.media.mit.edu/resources/KEMAR.html
Enjoy! :-)
Aureal did this back in 1998. I still have one of their cards, although I'd need Windows 98 to run it.
Imagine ray-tracing, but with audio. A game using the Aureal API would provide geometric environment information (e.g. a 3D map) and the audio card would ray-trace sound. It was exactly like hearing real things in the world around you. You could focus your eyes on the sound sources and attend to given sources in a noisy environment.
As I understand it, Creative destroyed Aureal by means of legal expenses in a series of patent infringement claims (which were all rejected).
In the public domain world, OpenAL exists - an audio version of OpenGL. I think development stopped a long time ago. They had a very simple 3D audio approach, no geometry - no better than EAX in software.
EAX 4.0 (and I think there is a later version?) finally - after a decade - I think have incoporated some of the geometric information ray-tracing approach Aureal used (Creative bought up their IP after they folded).
The Source (Half-Life 2) engine on the SoundBlaster X-Fi already does this.
It really is something to hear. You can definitely hear the difference between an echo against concrete vs wood vs glass, etc...
A little known side area is voip. While games are having actively developed software, you are likely to spent time talking to others while you are gaming as well.
Mumble ( http://mumble.sourceforge.net/ ) is software that uses plugins to determine who is ingame with you. It will then position its audio in a 360 degree area around you, so the left is to the left, behind you sounds like as such. This made a creepily realistic addition, and while trying it out it led to funny games of "marko, polo".
Audio took a massive back turn in vista, where hardware was not allowed to be used to accelerate it anymore. This killed EAX as it was in the XP days. Software wrappers are gradually getting built now.
Very interesting field indeed. So interesting, that I'm going to do my master's degree thesis on this subject. In particular, it's use in first person shooters.
My literature research so far has made it clear that this particular field has little theoretical background. Not a lot of research has been done in this field, and most theory is based on movie-audio theory.
As for practical applications, I haven't found any so far. Of course, there are plenty titles and packages which support real-time audio-effect processing and apply them depending on the general surroundings of the auditor. e.g.: auditor enters a hall, so a echo/reverb effect is applied on the sound samples. This is rather crude. An analogy for visuals would be to subtract 20% of the RGB-value of the entire image when someone turns off (or shoots ;) ) one of five lightbulbs in the room. It's a start, but not very realisic at all.
The best work I found was a (2007) PhD thesis by Mark Nicholas Grimshaw, University of Waikato , called The Accoustic Ecology of the First-Person Shooter
This huge pager proposes a theoretical setup for such an engine, as well as formulating a wealth of taxonomies and terms for analysing game-audio. Also he argues that the importance of audio for first person shooters is greatly overlooked, as audio is a powerful force for emergence into the game world.
Just think about it. Imagine playing a game on a monitor with no sound but picture perfect graphics. Next, imagine hearing game realisic (game) sounds all around you, while closing your eyes. The latter will give you a much greater sense of 'being there'.
So why haven't game developers dove into this full-hearted already? I think the answer to that is clear: it's much harder to sell. Improved images is easy to sell: you just give a picture or movie and it's easy to see how much prettier it is. It's even easily quantifyable (e.g. more pixels=better picture). For sound it's not so easy. Realism in sound is much more sub-conscious, and therefor harder to market.
The effects the real world has on sounds are subconsciously percieved. Most people never even notice most of them. Some of these effects cannot even conciously be heard. Still, they all play a part in the percieved realism of the sound. There is an easy experiment you can do yourself which illustrates this. Next time you're walking on the sidewalk, listen carefully to the background sounds of the enviroment: wind blowing through leaves, all the cars on distant roads, etc.. Then, listen to how this sound changes when you walk nearer or further from a wall, or when you walk under an overhanging balcony, or when you pass an open door even. Do it, listen carefully, and you'll notice a big difference in sound. Probably much bigger than you ever remembered.
In a game world, these type of changes aren't reflected. And even though you don't (yet) consciously miss them, your subconsciously do, and this will have a negative effect on your level of emergence.
So, how good does audio have to be in comparison to the image? More practical: which physical effects in the real world contribute the most to the percieved realism. Does this percieved realism depend on the sound and/or the situation? These are the questions I wish to answer with my research. After that, my idea is to design a practical framework for an audio engine which could variably apply some effects to some or all game audio, depending (dynamically) on the amount of available computing power. Yup, I'm setting the bar pretty high :)
I'll be starting per September 2009. If anyone's interested, I'm thinking about setting up a blog to share my progress and findings.
Janne Louw
(BSc Computer Sciences Universiteit Leiden, The Netherlands)