I'm wanting to implement an HMM (Hidden Markov Model) in order to identify particular words. So far, I have managed to extract the Coefficients (MFCC) of the signal and wondered if this is ok data in order to train the HMM?
Also, is the format (below) correct for training the HMM?
The format:
Foreach sample, there are a sequence of MFCC Coefficients, I have provided two of these samples as an example...
-13.8033 0.645476 3.2174 -0.625136 -0.470134 -2.96368 0.701151 0.464246 1.1898 -1.88515 0.0805242 0.311573 0.732487
-19.4252 -5.65454 0.853437 0.317219 0.146167 -1.93742 0.381944 -2.01793 -0.561144 -0.896783 -0.105491 -1.06504 -0.797318
Hope someone can help :)
You can have two approaches.
One is doing vector quantization on those vectors in order to convert the continuos MFCC vectors into discretes observations for the HMM.
Other is perform the training in HMM using a continuos approach.
You can see more in this thread:
Simple speech recognition from scratch
Related
In this dataset, I wanted to use signals with same unit and different SNR as input signals in ICA, i.e.
ica_input = np.array([ record_118e_6(MLII),
record_118e00(MLII),
record_118e06(MLII),
record_118e12(MLII),
record_118e18(MLII)
])
Is this a correct input to ICA?
Can I here consider the signal with different SNR linear mix of noises and true signal?
According to this article (which states how the dataset is generated by nst), The above input channels are linear mix of noise and clean signal and from plotting one can see that this data is clearly non gaussian hence ICA can be used in this case. Please correct me if I am wrong.
I am trying to implement time series forecasting using genetic programming. I am creating random trees (Ramped Half-n-Half) with s-expressions and evaluating each expression using RMSE to calculate the fitness. My problem is the training process. If I want to predict gold prices and the training data looked like this:
date open high low close
28/01/2008 90.959999 91.889999 90.75 91.75
29/01/2008 91.360001 91.720001 90.809998 91.150002
30/01/2008 90.709999 92.580002 90.449997 92.059998
31/01/2008 90.919998 91.660004 90.739998 91.400002
01/02/2008 91.75 91.870003 89.220001 89.349998
04/02/2008 88.510002 89.519997 88.050003 89.099998
05/02/2008 87.900002 88.690002 87.300003 87.68
06/02/2008 89 89.650002 88.75 88.949997
07/02/2008 88.949997 89.940002 88.809998 89.849998
08/02/2008 90 91 89.989998 91
As I understand, this data is nonlinear so my questions are:
1- Do I need to make any changes to this data like exponential smoothing? and why?
2- When looping the current population and evaluating the fitness of each expression on the training data, should I calculate the RMSE on just part of this data or all of it?
3- When the algorithm finishes and I get an expression with the best (lowest) fitness, does this mean that when I apply any row from the training data, the output should be the price of the next day?
I've read some research papers about this and I noticed some of them mentioning dividing the training data when calculating the fitness and some of them are doing exponential smoothing. However, I found them a bit difficult to read and understand, and most implementations I've found are either in python or R which I am not familiar with.
I appreciate any help on this.
Thank you.
I have 2 features: 'Contact_Last_Name' and 'Account_Last_Name' based on which I want to Classify my data:
The logic is that if the 2 features are same i.e. Contact_Last_Name is same as Account_Last_Name - then the result is 'Success' or else it is 'Denied'.
So. for example: if Contact_Last_Name is 'Johnson' and Account_Last_Name is 'Eigen' - the result is classified as 'Denied'. If both are equal say - 'Edison' - then the result is 'Success'.
How, can I have a Classification algorithm for this set of data?
[please note that usually we discard High Correlation columns but over here the correlation between columns seems to have the logic for Classification]
I have tried to use Decision Tree(C5.0) and Naive Bayes(naiveBayes) in R but both of these fail to Classify the dataset correctly.
First of all its not a good use case for machine learning, because this can be done with just string match, but still if you want to give to a classification algorith, then create a table with values as 'Contact_Last_Name' and 'Account_Last_Name' and 'Result' and give it for decision tree and predict the third column.
Note that you partition your data for training and testing.
I have an image with 8 channels.I have a conventional algorithm where weights are added to each of these channels to get an output as '0' or '1'.This works fine with several samples and complex scenarios. I would like implement the same in Machine Learning using CNN method.
I am new to ML and started looking out the tutorials which seem to be exclusively dealing with image processing problems- Hand writing recognition,Feature extraction etc.
http://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/
https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/neural_networks.html
I have setup the Keras with Theano as background.Basic Keras samples are working without problem.
What steps do I require to follow in order achieve the same result using CNN ? I do not comprehend the use of filters,kernels,stride in my use case.How do we provide Training data to Keras if the pixel channel values and output are in the below form?
Pixel#1 f(C1,C2...C8)=1
Pixel#2 f(C1,C2...C8)=1
Pixel#3 f(C1,C2...C8)=0 .
.
Pixel#N f(C1,C2...C8)=1
I think you should treat this the same way you use CNN to do semantic segmentation. For an example look at
https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf
You can use the same architecture has they are using but for the first layer instead of using filters for 3 channels use filters for 8 channels.
For the loss function you can use the same loos function or something that is more specific for binary loss.
There are several implementation for keras but with tensorflow
backend
https://github.com/JihongJu/keras-fcn
https://github.com/aurora95/Keras-FCN
Since the input is in the form of channel values,that too in sequence.I would suggest you to use Convolution1D. Here,you are taking each pixel's channel values as the input and you need to predict for each pixel.Try this
eg :
Conv1D(filters, kernel_size, strides=1, padding='valid')
Conv1D()
MaxPooling1D(pool_size)
......
(Add many layers as you want)
......
Dense(1)
use binary_crossentropy as the loss function.
I am using jlibsvm to do SVM for regression .My data set is very small (42 samples) . When I use the dataset to create the model using epsilon SVR with sigmoid kernel then no support vectors are generated.
This is what I get in my model file :
svm_type epsilon_svr
kernel_type sigmoid
gamma 0.02380952425301075
coef0 0.0
label
rho -66.42803
total_sv 0
probA -1.0
SV
When I use some other data set on the libsvm website I get a model file with support vectors fine.
Can someone please suggest why no support vectors are being generated for my data set ?
My data set file is formatted right so no issues there...
This could mean that the best found classification, given your data and the hyperparameters, is to assign the same label to all samples.
Are your samples unbalanced? What's the number of positive and negative samples? You might want to try to add a weighting to positive/negative samples to account for that
It could also be the samples are hard to separate given their structure and the kernel type. Have you tried a different structure?
With only 42 data samples, maybe you could add them to your question and get better answers.