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A list of future ideas for the musical recognition project.

A number of changes and additions to this project would help it to scale better and be more statistically accurate. Such changes should help the project to handle more complex signals and operate over a larger number of musical instruments.

Improving the gaussian mixture model

To improve the statistical accuracy, the Gaussian Mixture Model used in this project must improve. The features of this model help determine its accuracy, and choosing appropriate additional features is a step towards improving the project. These features may include modeling additional temporal, spectral, harmonic and perceptual properties of the signals, and will help to better distinguish between musical instruments. Temporal features were left out of this project, as they are difficult to analyze in polyphonic signals. However, these features are useful in distinguishing between musical instruments. Articulation, in particular, is useful in distinguishing a trumpet sound, and articulation is by its very nature a temporal feature.

Additionally, more analysis of what features are included in the Gaussian Mixture Model is necessary to improve the statistical accuracy. Too many features, or features that do not adequately distinguish between the instruments, can actually diminish the quality of the output. Such features could respond to the environment noise in a given signal, or to differences between players on the same instrument, more easily than they distinguish between instruments themselves, and this is not desirable. Ideally, this project would involve retesting the sample data with various combinations of feature sets to find the optimal Gaussian Mixture Model.

Improving training data

As training data for this experiment, we used chromatic scales for each instrument over its entire effective range, taken in a single recording session in a relatively low noise environment. To improve this project, the GMM should be trained with multiple players on each instrument, and should include a variety of music - not just the chromatic scale. It should also inlude training data from a number of musical environments with varying levels of noise, as the test data that later is passed through the GMM can hardly be expected to be recorded under the same conditions as the training recordings.

Additionally, the training of the GMM would be improved if it could be initially trained on some polyphonic signals, in addition to the monophonic signals that it is currently trained with. Polyphonic training data was left out of this project due to the complexity of implementation, but it could improve the statistical accuracy of the GMM when decomposing polyphonic test signals.

Increasing the scope

In addition to training the GMM for other players on the three instruments used in this project, to truly decode an arbitrary musical signal, additional instruments must be added. This includes other woodwinds and brass, from flutes and double reeds to french horns and tubas, to strings and percussion. The GMM would likely need to extensively train on similar instruments to properly distinguish between them, and it is unlikely that it would ever be able to distinguish between the sounds of extremely similar instruments, such as a trumpet and a cornet, or a baritone and a euphonium. Such instruments are so similar that few humans can even discern the subtle differences between them, and the sounds produced by these instruments vary more from player to player than between, say, a trumpet and a cornet.

Further, the project would need to include other families of instruments not yet taken into consideration, such as strings and percussion. Strings and tuned percussion, such as xylophones, produce very different tones than wind instruments, and would likely be easy to decompose. Untuned percussion, however, such as cymbals or a cowbell, would be very difficult to add to this project without modifying it, adding features specifically to detect such instruments. Detecting these instruments would require adding temporal features to the GMM, and would likely entail adding an entire beat detection system to the project.

Improving pitch detection

For the most part, and especially in the classical genre, music is written to sound pleasing to the ear. Multiple notes playing at the same time will usually be harmonic ratios of one another, either thirds, or fifths, or octaves. With this knowledge, once we have determined the pitch of the first note, we can determine what pitch the next note is likely to be. Our current system detects the pitch at each window without any dependence on the previously detected note. A better model would track the notes and continue detecting the same pitch until the note ends. Furthermore, Hidden Markov Models have been shown useful in tracking melodies, and such a tracking system could also be incorporated for better pitch detection.

Questions & Answers

where we get a research paper on Nano chemistry....?
Maira Reply
what are the products of Nano chemistry?
Maira Reply
There are lots of products of nano chemistry... Like nano coatings.....carbon fiber.. And lots of others..
learn
Even nanotechnology is pretty much all about chemistry... Its the chemistry on quantum or atomic level
learn
Google
da
no nanotechnology is also a part of physics and maths it requires angle formulas and some pressure regarding concepts
Bhagvanji
Preparation and Applications of Nanomaterial for Drug Delivery
Hafiz Reply
revolt
da
Application of nanotechnology in medicine
what is variations in raman spectra for nanomaterials
Jyoti Reply
I only see partial conversation and what's the question here!
Crow Reply
what about nanotechnology for water purification
RAW Reply
please someone correct me if I'm wrong but I think one can use nanoparticles, specially silver nanoparticles for water treatment.
Damian
yes that's correct
Professor
I think
Professor
Nasa has use it in the 60's, copper as water purification in the moon travel.
Alexandre
nanocopper obvius
Alexandre
what is the stm
Brian Reply
is there industrial application of fullrenes. What is the method to prepare fullrene on large scale.?
Rafiq
industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
Damian
How we are making nano material?
LITNING Reply
what is a peer
LITNING Reply
What is meant by 'nano scale'?
LITNING Reply
What is STMs full form?
LITNING
scanning tunneling microscope
Sahil
how nano science is used for hydrophobicity
Santosh
Do u think that Graphene and Fullrene fiber can be used to make Air Plane body structure the lightest and strongest. Rafiq
Rafiq
what is differents between GO and RGO?
Mahi
what is simplest way to understand the applications of nano robots used to detect the cancer affected cell of human body.? How this robot is carried to required site of body cell.? what will be the carrier material and how can be detected that correct delivery of drug is done Rafiq
Rafiq
if virus is killing to make ARTIFICIAL DNA OF GRAPHENE FOR KILLED THE VIRUS .THIS IS OUR ASSUMPTION
Anam
analytical skills graphene is prepared to kill any type viruses .
Anam
Any one who tell me about Preparation and application of Nanomaterial for drug Delivery
Hafiz
what is Nano technology ?
Bob Reply
write examples of Nano molecule?
Bob
The nanotechnology is as new science, to scale nanometric
brayan
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Damian
Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
Renato
What fields keep nano created devices from performing or assimulating ? Magnetic fields ? Are do they assimilate ?
Stoney Reply
why we need to study biomolecules, molecular biology in nanotechnology?
Adin Reply
?
Kyle
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
Adin
why?
Adin
what school?
Kyle
biomolecules are e building blocks of every organics and inorganic materials.
Joe
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Source:  OpenStax, Musical instrument recognition. OpenStax CNX. Dec 14, 2005 Download for free at http://cnx.org/content/col10313/1.3
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