I need an algorithm (a sample program code if possible) to compare sound patterns from two files(.wav or .mp3)..
I wish to build an application in c++ which will take two source files as input and compare them whether they are equal.
I searched the internet but did not get an algorithm.
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I'm working on a project for school.
The assignment is as follows:
Implement a sorting algorithm of your choosing in assembly (we are using the GNU Assembler). The input is a text-file with a series of numbers separated by newline.
I'm then trying to implement insertion sort.
I have already opened and read the file and i'm able to print the content to terminal.
My problem is now how to split each number from the file in order to compare and sort them.
I believe google is glowing at the moment due to my effort to find and answer (maybe I don't know what I need to type or where to look).
I have tried to get each character from the string, which i'm able to do BUT I don't know to put them together again as integers (we only have integers).
If anybody could help with some keywords to search for it would be much appreciated.
I am tasked with something seemingly trivial which is to
find out how "noisy" a given recording is.
This recording came about via a voice recorder, a
OLYMPUS VN-733 PC which was fairly cheap (I am not doing
advertisement, I merely mention this because I in no way
aim to do anything "professional" here, I simply need to
solve a seemingly simple problem).
To preface this, I have already obtained several datasets
from different outside locations, in particular parks or
near-road recordings. That is, the noise that exists at
these specific locations, and to then compare this noise,
on average, with the other locations.
In other words:
I must find out how noisy location A is compared to location
B and C.
I have made 1 minute recordings each so that at the
least the time span of a recording can be compared
to the other locations (and I was using the very
same voice record at all positions, in the same
height etc...).
A sample file can be found at:
http://shevegen.square7.ch/test.mp3
(This may eventually be moved lateron, it just serves as
example how these recordings may sound right now. I am
unhappy about the initial noisy clipping-sound, ideally
I'd only capture the background noise of the cars etc..
but for now this must suffice.)
Now my specific question is, how can I find out how "noisy"
or "loud" this is?
The primary goal is to compare them to the other .mp3
files, which would suffice for my purpose just fine.
But ideally it would be nice to calculate on average
how "loud" every individual .mp3 is and then compared
it to the other ones (there are several recordings
per given geolocation, so I could even merge them
together).
There are some similar questions but not one in particular
that I was able to find that could answer this in a
objective manner, or perhaps I did not understand the
problem at hand. I have all the audio datasets already
but I have no idea how to find out how "loud" any one
of them is individually; there are some apps on smartphones
that claim that they can do this automatically but since
I do not have any smartphone, this is a dead end for me.
Any general advice will be much appreciated.
Noise is a notion difficult to define. Then, I will focus on loudness.
You could compute the energy of each files. For that, you need to access the samples of the audio signal (generally from a built-in function of you programming language). Then you could compute the RMS energy of the signal.
That could be the more basic processing.
I have a text corpus which is already aligned at sentence level by construction - it is a list of pairs of English strings and their translation in another language. I have about 10 000 strings of 5 - 20 words each and their translations. My goal is to try to build a metric of the quality of the translation - automatically of course, because I'm dealing with languages I know nothing about :)
I'd like to build a dictionary from this list of translations that would give me the (most probable) translation of each word in the source English strings into the other language. I know the dictionary will be far from perfect but I'm hoping I can have something good enough to flag when a word is not consistently translated, for example, if my dictionary says "Store" is to be tranlated into French by "Magasin" then if I spot some place where "Store" is translated as "Boutique" I can suspect that something is wrong.
So I'd need to:
build a dictionary from my corpus
align the words inside the string/translation pairs
Do you have good references on how to do this? Known algorithms? I found many links about text alignment but they seem to be more at the sentence level than at the word level...
Any other suggestion on how to automatically check whether a translation is consistent would be greatly appreciated!
Thanks in advance.
A freely available (specifically, GPL-licensed) tool for word alignment is GIZA++. I trains the well-known IBM models mentioned in other answers, as well as other statistical models.
You can download it from the GIZA++ site at Google Code, and there is a brief introduction to its usage found at the GIZA++ Apertium. It boils down to this procedure:
Create your parallel corpus, sentence-aligned (you seem to have this already)
Apply the plain2snt tool included in GIZA++ to extract word lists and sentence lists in GIZA++ format
(Optional – only used for some models:) Generate word classes using the mkcls tool (also included)
Run the actual word alignment tool GIZA++. There are various optional configuration settings you can use to determine the type of model generated.
Before you can do this, you must build the tool from source code by running make. The code is written in C++ and compiles well with recent GCC versions.
A few final notes:
There are more than one possible translations for every word; you shouldn't rely on the assumption that a specific translation found in one text is necessarily wrong just because the same word is translated differently in another text;
One word may be translated into a (not necessarily contiguous) sequence of several words, and vice versa. Some words are not translated at all;
GIZA++ is a statistical tool that approximates the correct word alignment; many of the alignments it generates are questionable or incorrect.
This a pretty standard statistical machine translation problem called 'word alignment'.
There are bunch of EM clustering-based models developed by researchers at IBM which I think are the base for most other cooler models being developed today.
Google for 'ibm word alignment models' to find about IBM Models 1 to 5.
This presentation - http://www.stanford.edu/class/cs224n/handouts/cs224n-lecture-05-2011-MT.pdf seems like a good place to start.
Are you using spaces between words? Whatever character you are using, you might check out the slice command in Linux. It gives you the ability to filter words in-between spaces and other characters.
I am having some trouble getting pointers to how to perform what appears to be a deceptively easy task:
Given an audio stream, how do you count the number of words that have been spoken, in real-time?
I don't need to recognize what the words are, but rather just have an accurate counter on words that have been uttered. The counter doesn't have to be too accurate and could even consider utterances and other "grunts" like coughs.
It appears that all Speech Recognition systems depend on a pre-defined grammar to be provided before they can analyze the phonemes that are spoken to convert to known words with some degree of accuracy. But I don't care about the accuracy at all, but rather the rate of words being spoken.
What is important is that this runs in real time, and allow the system to provide alerts after a certain number of words have been spoken. The system will encourage a visual cue to pause, and then the speaker can continue.
I've looked at CMU Sphinx FAQ and found that the idea of "word spotting" is not yet supported. I don't really need a real time search of particular words, but it approximates more closely to what I am looking for. Looking for very small silences in the waveform seems to be a very crude way of doing this and probably not very accurate at all, but that's all I have right now.
Any pointers on algorithms, research papers or any other insights would be appreciated!
I have come up with an idea for an audio project and it looks like Go is a useful language for implementing it. However, it requires the ability to apply filters to incoming audio, and Go doesn't appear to have any sort of audio processing package. I can use cgo to call C code, but every signal processing library I find uses C++ classes which cgo cannot handle. It looks like libsox may work. Are there any others?
What libsox can provide and what I need is to take an incoming audio stream and divide it into frequency bands. If I can do this while only reading the file once, then bonus! I am not sure if libsox can do this.
If you want to use a C++ library you could try SWIG, but you'll have to get it out of Subversion. The next release (2.0.1) will be the first released version to support Go. In my experience the Go support is still a little rough, but then again the library I tried to wrap is a monster.
Alternatively, you could still create your own bindings through cgo using the same method SWIG does, but it will be painful and tedious. The basic idea is that you first create a C wrapper, then let cgo create a Go wrapper around your C wrapper.
I don't know anything about signal processing or libsox, though. Sorry.
There is a relatively new project called ZikiChombo
which contains so far some basic DSP functionality geared toward audio, see here
The dsp part of the project has filters on its roadmap, but they are not yet there. On the other hand some infrastructure for implementing filters, such as real fft and block convolution is there. Meaning that if you want FIRs, and can compute the coefficients by some other means, you can run them via convolution in zc currently with sound in real time.
Basic filtering design support (FIR,Biquad), for example using an ideal filter as a starting point will be the next step for zc. There are numerous small self-contained open source projects for basic and more advanced FIR and IIR filter design, most notably Iowa Hills which might be more accessible than a larger project to compute filter coefficients outside of Go.
More advanced filtering such as Butterworth, and filters based on polynomial solving and the bilinear transform will take more time for zc.
There is also some software defined radio Golang projects with some code related to filtering, sorry don't have the links offhand but a search for the topic may lead you to them.
Finally, there is a gonum Fourier package which also supplies fft.
So Go is growing some interesting and potentially stuff in this domain, but still has quite a ways to go compared to older projects (which are mostly in C/C++, or perhaps with a Python wrapper via numpy for example).
I am using this pure golang repo to perform Fourier Transforms with good effect
https://github.com/mjibson/go-dsp
just supply the FFT call with a
import (
"github.com/mjibson/go-dsp/fft" // https://github.com/mjibson/go-dsp
)
var audio_wave []float64
// ... now populate audio_wave with your audio PCM samples
var complex_fft []complex128
// input time domain ... output frequency domain of equally spaced freq bins
complex_fft = fft.FFTReal(audio_wave)