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This module exists solely to tie up loose ends left over from the previous modules. More precisely, in this module may be found the Matlab code and workspace used for the purposes of this project and the sample waveforms against which we tested our program. First, though, a discussion on up-sampling:

Up-sampling

Up-sampling (that is, representing something with few samples as something with many samples) is relatively straight forward when one deals with rational multiples. First, one converts the signal into the frequency domain using the Fast Fourier Transform discussed in a previous module. The samples are then "spread out" (zeros are added) based on the rational multiple by which one is up-sampling. Then a low-pass filter and IFFT later, you're back to an up-sampled version of the original signal.

Limitations

No project is complete without first recognizing the limitations inherent in whatever was accomplished. The most significant drawback in our program in terms of realizing our final goal is the lack of automating the threshold detection. Without an "intelligent" program, perhaps based on a neural network, we have little hope of filtering out a particular instrument in the data representing a full orchestra. We are quite capable, however, of detecting multiple instruments so long as the multiple is not too large and we are allowed to set the threshold ourselves.

The computational complexity increases with the number of instruments (samples) tested. We create no explicit infrastructure to break a song into component tracks (at least conceptually) so that we may analyze each one against a particular set of samples representing a single instrument. Also, we would be well-advised to input the frequency domain representation of the samples to decrease computational complexity.

A further limitation is the need to input several samples from the same instrument. Ideally, we would input merely the sound of that instrument playing and modulate that one sound to create as wide a range of tones as was required. The idea here being that a given instrument has a unique frequency fingerprint that remains intact over all frequencies. This is not perfectly true (each instrument has its own idiosyncracies relating to its real-world implementation), but might prove accurate enough for our proposed analysis.

Future instrument and note recognition endeavors

And no limitations section is complete without some mention of how to surpass those limitations. The goal of any project is to refine the product to the point beyond which refinement is no longer possible. Because this is in practice impossible to accomplish, we will list a set of future "next steps" we or others who follow may be encouraged to take.

To intelligently detect the relative volume of noise in a given sample, one might best be served to create a statistical filter which recognizes random noise. This statistical filter would, in theory, identify the windows which most resemble random noise. From knowledge of which windows cause noise, one might derive the volume-level (read: power-level) associated with said noise and set the threshold at some point beyond that. The upper-bound of the threshold could be found as the lowest power value for any other non-noise (as indicated by the statistical filter) window.

The threshold detection for specific instruments is more complicated: our suggestion is to develop some method of correlation or detection as-of-yet unknown to these authors (but likely known to those who research these concepts). This method would likely match frequency domain signals rather than time domain (that is, match filtering two frequency domain representations; sort of a meta-Matched Filter in terms of FFTs) using some statistical algorithm.

The computation complexity issue is trivial to solve. One must simply code the infrastructure to analyze a given signal in several channels, each acting as our entire program now acts. To convert the samples into the frequency domain, one need only FFT each sample.

The final observed limitation, too, is within our grasp. We briefly attempted a method which is promising: Mellin transformation. Essentially, when one takes a signal and transforms it into the Mellin domain (by multiplying by an exponential), one is in the position to merely phase-shift the frequency domain representation to acheive a modulation. Thus, converting back from the Mellin domain after phase-shifting the original transformed signal changes one note into another (musical modulation). This also has ( many ) more applications than simply for our particular program. Image recognition over dilation comes most immediately to mind.

Relevant files

If you choose to use our files, we would like to be informed of their use. Not because we want to inhibit any potential use of our work but rather because we want to know our audience is more than a few trillion electrons searching the internet for googly content. Imitation is, after all, the sincerest form of flattery. We hope you find our work both enlightening and useful.

Matlab code

Our primary program.

Our output-processing program.

Our Up-Sampling program. (Expects a vector as input; outputs a vector).

Our Up-Sampling program. (Expects a struct as created from Matlab's "Import Data" feature when importing a .wav file as input; outputs a similar struct).

Clarinet samples

The samples used for analysis of the professional recordings (i.e. recordings sampled at 44100 Hz)

The samples used for analysis of the unprofessional recordings (i.e. recordings sampled at 22050 Hz)

One may convert these samples to any other sampling frequency by means of the up-sampling program. The samples cover from the lowest note on a Bb Clarinet (E in the chalameau register) to the highest C in the clarion register (right before reaching the altissimo register). The lowest three notes have questionable integrity (I choose to blame the microphone ;-) ).

Music files (signals)

A Chromatic Scale , as performed on clarinet by the up-and-coming clarinetist, Michael Lawrence.

Stravinsky's Three Pieces for Clarinet , unknown artist.

Barber's Adagio for Strings . , Kalman Opperman Clarinet Choir.

For our program to work, .mp3 files must first be decompressed into .wav files. We used a free program found on http://www.cnet.download.com . We would post the decompressed files but, as one might imagine, they are too large to post on Connexions.

Poster

Our Poster.

In the name of thoroughness, we include a copy of the poster created for an end-of-semester poster session show-casing our project. You should find a great deal of it familiar.

Questions & Answers

how do they get the third part x = (32)5/4
kinnecy Reply
can someone help me with some logarithmic and exponential equations.
Jeffrey Reply
sure. what is your question?
ninjadapaul
20/(×-6^2)
Salomon
okay, so you have 6 raised to the power of 2. what is that part of your answer
ninjadapaul
I don't understand what the A with approx sign and the boxed x mean
ninjadapaul
it think it's written 20/(X-6)^2 so it's 20 divided by X-6 squared
Salomon
I'm not sure why it wrote it the other way
Salomon
I got X =-6
Salomon
ok. so take the square root of both sides, now you have plus or minus the square root of 20= x-6
ninjadapaul
oops. ignore that.
ninjadapaul
so you not have an equal sign anywhere in the original equation?
ninjadapaul
Commplementary angles
Idrissa Reply
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Sherica
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Uday
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a perfect square v²+2v+_
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algebra 2 Inequalities:If equation 2 = 0 it is an open set?
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or infinite solutions?
Kim
The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
Al
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Embra Reply
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Nancy Reply
rolling four fair dice and getting an even number an all four dice
ramon Reply
Kristine 2*2*2=8
Bridget Reply
Differences Between Laspeyres and Paasche Indices
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No. 7x -4y is simplified from 4x + (3y + 3x) -7y
Mary Reply
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. After 3 months on a diet, Lisa had lost 12% of her original weight. She lost 21 pounds. What was Lisa's original weight?
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Cied
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I start with an easy one. carbon nanotubes woven into a long filament like a string
Porter
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Porter
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Yasmin
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Cesar
I'm interested in nanotube
Uday
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what is system testing?
AMJAD
preparation of nanomaterial
Victor Reply
Yes, Nanotechnology has a very fast field of applications and their is always something new to do with it...
Himanshu Reply
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AMJAD
what is system testing
AMJAD
what is the application of nanotechnology?
Stotaw
In this morden time nanotechnology used in many field . 1-Electronics-manufacturad IC ,RAM,MRAM,solar panel etc 2-Helth and Medical-Nanomedicine,Drug Dilivery for cancer treatment etc 3- Atomobile -MEMS, Coating on car etc. and may other field for details you can check at Google
Azam
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Prasenjit
after 100 year this will be not nanotechnology maybe this technology name will be change . maybe aftet 100 year . we work on electron lable practically about its properties and behaviour by the different instruments
Azam
name doesn't matter , whatever it will be change... I'm taking about effect on circumstances of the microscopic world
Prasenjit
how hard could it be to apply nanotechnology against viral infections such HIV or Ebola?
Damian
silver nanoparticles could handle the job?
Damian
not now but maybe in future only AgNP maybe any other nanomaterials
Azam
Hello
Uday
I'm interested in Nanotube
Uday
this technology will not going on for the long time , so I'm thinking about femtotechnology 10^-15
Prasenjit
can nanotechnology change the direction of the face of the world
Prasenjit Reply
At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
Ali Reply
the Beer law works very well for dilute solutions but fails for very high concentrations. why?
bamidele Reply
how did you get the value of 2000N.What calculations are needed to arrive at it
Smarajit Reply
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Source:  OpenStax, Instrument and note identification. OpenStax CNX. Dec 14, 2004 Download for free at http://cnx.org/content/col10249/1.1
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