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This module discusses secondary and/or experimental detection methods used in a real-time laugh track removal system. This module is part of a larger series discussing the implementation of this system.


In this module, some of the more exotic detection methods considered for our laugh track removal system are discussed. In particular, a detection method using polynomial curve fitting in conjunction with support vector machines.

Support vector machine (svm)/polynomial curve fitting

As previously discussed in the Anatomy of a Laugh Track module, laugh tracks have a characteristic shape in the time domain. In order to take advantage of this fact, one detection method we considered involved using support vector machines and polynomial curve fitting to detect laughs. Given the unique shape of a laugh track, one ought to be able to consistently fit the same polynomial curve onto a signal representing a laugh. This curve can then be completely characterized in terms of its coefficients. By then building a database of positive and negative examples of polynomial coefficients, and training a support vector machine on this data, we ought to then be able to produce a fast detection scheme for laughs.

What are support vector machines?

Support vector machines are a relatively new technique for partitioning high dimensional datasets into classes. At the simplest level, a SVM divides a high dimensional dataset using hyper-planes. These divisions can then be completely characterized in terms of the data points–called support vectors–that are closest to the hyper-plane of the division. In this way an enormously complex dataset can be partitioned, and more importantly, these partitions are easy to describe. Once a training dataset is partitioned, new points can be classified by checking into which partition the point falls. Further complexity can be introduced by switching from hyper-planes to other curves for partitioning a dataset. Common curves include polynomials, sigmoids, and radial basis functions. More information about support vector machines can be found at www.kernel-machines.org .

Polynomial curve fitting

Not to be confused with the polynomial curve potentially used by a SVM, we experimented using polynomial curve fitting to characterize the shape of a laugh track. After experimenting with a handful of potential curves, we chose the degree 9 polynomial as the best curve to use for fitting based on the low error, quick fitting, and high dimension. The high dimension is important, because the power of SVMs becomes most readily apparent in high dimensional space. In this case, our data consisted of 11 dimensional vectors: 9 dimensions from the coefficients of a fitted polynomial plus the duration of the audio segment in question, and the error of the fitted curve.


In order to detect laugh tracks, we first divided the audio stream into smaller segments which we then approximated with a polynomial curve and stored into a database for use in a SVM training regime. Observation revealed that the shortest laugh track is approximately 1 second long. To generate our dataset, we considered audio segments spaced slightly apart (0.25 to 0.33 seconds) of at least 1 second in duration. Having fit a curve to the 1 second segment, we then examined a segment slightly longer. If the error of approximation of the longer segment was better, then we considered an even long segment, until an optimal segment was found. The coefficients of the polynomial which was fit, the duration, the error, and the classification (laugh, not laugh), were then stored into a dataset.

Having built a sufficiently large training dataset, we then trained a support vector machine ( LIBSVM ). Experimentation with parameters showed that the radial basis kernel produced the best results. New audio segments were then classified according to the partitions generated.

Results and analysis

Overall, the results of this detection method were reasonable, although not ideal. In particular, a 70% detection rate was achieved. Further analysis revealed several factors limited the effectiveness of this method. First, the large number of negative to positive examples heavily skewed the dataset. Experiments showed that results are exceedingly sensitive to parameter selection due to this fact. Second, a large amount of human error existed in the dataset. The training dataset had to be constructed by hand, and variability as to what did and did not qualify as a laugh introduced error. Finally, it is questionable as to how suitable polynomial coefficients are for classifying shape. Coefficient values do not necessarily encode shape characteristics such as duration, slope, and amplitude directly, and as such classification along these lines may be difficult. In sum, this detection method was reasonable, but produced results that were worse than other, simpler methods.

Questions & Answers

where we get a research paper on Nano chemistry....?
Maira Reply
nanopartical of organic/inorganic / physical chemistry , pdf / thesis / review
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..
Even nanotechnology is pretty much all about chemistry... Its the chemistry on quantum or atomic level
no nanotechnology is also a part of physics and maths it requires angle formulas and some pressure regarding concepts
Preparation and Applications of Nanomaterial for Drug Delivery
Hafiz Reply
Application of nanotechnology in medicine
what is variations in raman spectra for nanomaterials
Jyoti Reply
ya I also want to know the raman spectra
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.
yes that's correct
I think
Nasa has use it in the 60's, copper as water purification in the moon travel.
nanocopper obvius
what is the stm
Brian Reply
is there industrial application of fullrenes. What is the method to prepare fullrene on large scale.?
industrial application...? mmm I think on the medical side as drug carrier, but you should go deeper on your research, I may be wrong
How we are making nano material?
what is a peer
What is meant by 'nano scale'?
What is STMs full form?
scanning tunneling microscope
how nano science is used for hydrophobicity
Do u think that Graphene and Fullrene fiber can be used to make Air Plane body structure the lightest and strongest. Rafiq
what is differents between GO and RGO?
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
analytical skills graphene is prepared to kill any type viruses .
Any one who tell me about Preparation and application of Nanomaterial for drug Delivery
what is Nano technology ?
Bob Reply
write examples of Nano molecule?
The nanotechnology is as new science, to scale nanometric
nanotechnology is the study, desing, synthesis, manipulation and application of materials and functional systems through control of matter at nanoscale
Is there any normative that regulates the use of silver nanoparticles?
Damian Reply
what king of growth are you checking .?
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
yes I'm doing my masters in nanotechnology, we are being studying all these domains as well..
what school?
biomolecules are e building blocks of every organics and inorganic materials.
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Source:  OpenStax, Elec 301 projects fall 2007. OpenStax CNX. Dec 22, 2007 Download for free at http://cnx.org/content/col10503/1.1
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