I am a new Matlab user and I would be grateful if you help me. I have converted a set of time series into pictural presentations using CWT (continuous wavelet transform) and trained a deep learning network with quite a reasonable accuracy. I have made use of classify to check the trained network performance for the output of a single image. Now I am going to use it for a series of images consecutively feeding on the main time series, so how do I have to use classify in this issue?
regards
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I am new to learning and training neural networks. I have the task of recognizing emotions based on voice data (audio data). I'm trying to create the simplest recurrent network in Matlab. I am submitting images to the input in the form of spectrograms. However, the accuracy of the network is about 40%. In fact, I understand that the result obtained is not acceptable. I ask you for advice and recommendations on how to dig in which direction to achieve a more significant result.
Looking for some advice. I am playing around with an accelerometer, combined with the machine learning app in matlab. Clearly there are many ways to extract features from the received data, both in time and frequency domains. However, I have recently come across time-frequency analysis, specifically using wavelets.
Has anyone got any advice on using wavelet analysis for classifying accelerometer (or similar) data and the benefits of using it ? Or if indeed this would be a valid way of extracting features ? I'm not too sure what sort of data I should be extracting using this method ?
Thanks in advance.
Few points to note,
1)You can transform a number of samples (should be a dyadic number and depends on your sampling frequency) into wavelet domain and classify that data. (eg. if you transform 64 accelerometer samples then you also have 64 points in wavelet domain).
2) Apart from time-frequency information from wavelet transformation, wavelet transformation has sparsity property
(https://en.wikipedia.org/wiki/Sparse_approximation) that would be useful for your classification model.
3) Also, you can try different wavelet basis functions (mother wavelets),
and try to figure out which basis is most suitable for your data. Maybe you can start with Haar basis function as it is more suitable to capture the singular behaviour of your data.
I have a dataset of 60 points. I have supplied 58 points as input data to a NAR network in Matlab(using NNToolbox) and tried developing a model which would help me forecast the next two values. I wish to know how to obtain the next two forecasted values generated by the model in Matlab using NNToolbox. I didn't find any option in NNToolbox for the same.
Any help is highly appreciated.
I do not know much about NNToolbox in MATLAB, but for your dataset, I believe that you would need to train a Recurrent Neural Net (RNN) with the training examples. A RNN should forecast (by regression) the values generated by the model. And there are many tutorials available for building a RNN for regression.
I'm currently trying to create and train a neural network to perform simple speech classification using MFCCs.
At the moment, I'm using 26 coefficients for each sample, and a total of 5 different classes - these are five different words with varying numbers of syllables.
While each sample is 2 seconds long, I am unsure how to handle cases where the user can pronounce words either very slowly or very quickly. E.g., the word 'television' spoken within 1 second yields different coefficients than the word spoken within two seconds.
Any advice on how I can solve this problem would be much appreciated!
I'm currently trying to create and train a neural network to perform simple speech classification using MFCCs.
Simple neural networks do not have input lenght invariance and do not allow to analyze time series.
For classification of time series like a series of MFCC frames you can use a classifier with time invariance. For example you can use neural networks combined with hidden Markov models (ANN-HMM), gaussian mixture model with hidden markov models (GMM-HMM) or recurrent neural networks (RNN). Matlab implementation for RNN is here. Theano implementation is also available. You can find a detailed description of those structures in Google.
Speech recognition is not a simple thing to implement, it is better to use existing software like CMUSphinx
I would like to ask for ideas what options there is for training a MATLAB ANN (artificial neural network) continuously, i.e. not having a pre-prepared training set? The idea is to have an "online" data stream thus, when first creating the network it's completely untrained but as samples flow in the ANN is trained and converges.
The ANN will be used to classify a set of values and the implementation would visualize how the training of the ANN gets improved as samples flows through the system. I.e. each sample is used for training and then also evaluated by the ANN and the response is visualized.
The effect that I expect is that for the very first samples the response of the ANN will be more or less random but as the training progress the accuracy improves.
Any ideas are most welcome.
Regards, Ola
In MATLAB you can use the adapt function instead of train. You can do this incrementally (change weights every time you get a new piece of information) or you can do it every N-samples, batch-style.
This document gives an in-depth run-down on the different styles of training from the perspective of a time-series problem.
I'd really think about what you're trying to do here, because adaptive learning strategies can be difficult. I found that they like to flail all over compared to their batch counterparts. This was especially true in my case where I work with very noisy signals.
Are you sure that you need adaptive learning? You can't periodically re-train your NN? Or build one that generalizes well enough?