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I want to create a model that can predict who has speak with different word.
In this case i try to use feature
Mfcc
Melspectogram
Tempo
Chroma stft
Spectral Centroid
Spectral Bandwidth
Tempo
And for train that i am use RandomforestRegressor
It's possible to create model like that?
For the sound processing and feature extraction part, librosa is definitely going to provide you all you need.
For the machine learning part however, speaker identification (also called "voice recognition") is a relatively complex task. You probably will get more success using techniques from deep learning. You can certainly try to use random forests if you like, but you'll probably get a lower accuracy and will have to spend more time doing feature engineering. In fact, it will be a good exercise for you to compare the results you can get with the various techniques.
For an example tutorial on speaker identification using Keras, see e.g. this article.
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I have seen it on Shutterstock or on many websites. if you upload an image with automatically generate suggested tags.
That's commonly done using (Deep) Artificial Neural Networks (NNs).
The idea is that you feed an image into a trained NN model and it will predict a classification of the entire image, detect objects present in the image, or even label regions inside the image. There's a lot of freedom in what can be achieved. Since good models are not easy to obtain (without large amounts of data and intense training resources), there exist pretrained models that can be finetuned by the user in order to make it work on your own particular dataset (unfortunately, these models are often somewhat overfit to the dataset they have been trained on such that finetuning is necessary most of the time). I think this link will point you further into the direction how these automatically suggested tags can be generated.
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While reading the great book by F. Chollet, I'm experimenting with Keras / Tensorflow, on a simple Sequential model that I train on simulated images, which come from a physical analytical model.
Having full control of the simulations, I wrote a generator which produces an infinite stream of data and label batches, which I use with fit_generator in Keras. The data so generated are never identical, plus I can add some random noise to each image.
Now I'm wondering: is it a problem if the model never sees the same input data from one epoch to the next?
Can I assume my problems in getting the loss down are not due to the fact that the data are "infinite" (so I only have to concentrate on hyper parameters tuning)?
Please feel free if you have any advice for dealing with DL on simulated data.
A well trained network will pick up on patterns in the data, prioritizing new data over old. If your data comes from a constant distribution this doesn't matter, but if that distribution is changing over time it should adapt (slowly) to the more recent distribution.
The fact that the data is never identical does not matter. Most trained networks use some form of data augmentation (e.g. for image processsing, it is common for images to be randomly cropped, rotated, resized, and have color manipulations applied etc, so each example is never identical even if it comes from the same base image).
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I am planning on building a gender classifier. I know the two popular models are tf-idf and word2vec.
While tf-idf focuses on the importance of a word in a document and similarity of documents, word2vec focuses more on the relationship between words and similarity between them.
However none of theme seem to be perfect for building vector features to be used for gender classification. Is there any other alternative vectorization model that might suit this task?
Yes, there is another alternative to w2v: GloVe.
GloVe stands for Global Vector Embeddings.
As someone who has used this technique before to good effect, I would recommend GloVe.
GloVe optimally trains neural word embeddings not just by looking at local windows but considering a much larger width (30+ size), thereby embedding a much deeper level of semantics to the embedding.
With glove, it is easy to model relationships such as: X[man] - X[woman] = X[king] - X[queen], where these are all vectors.
Credits: GloVe GitHub page (linked below).
You can train your own GloVe embeddings, or you may use their retrained models available. Even for specific domains, the general models seem to work reasonably well, although you would get a lot more out of your models if you trained them yourself. Please look at the GitHub page for instructions on how to train your own models. It is very easy.
Additional reading:
GloVe: Global Vectors for Word Representation
GloVe repository
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I am trying to train a HAAR Cascade for car detection from a Drone.
Because of the viewing angle of the drone, I need to detect the car from many angles, So I need to train the classifier for that.
I have many 3d car models of the cars I want to detect, Can I use them to train the classifier instead of getting images from the internet ?
The car may be not moving, so I can't use motion as a parameter.
there are many questions here.
First, to train for many angles seems to be not really optimal for me, maybe you could check some more simple approaches like in this paper about car detection
https://www.tnt.uni-hannover.de/papers/data/977/scia2013_baumann.pdf
Second, yes, you can train on synthetic data, but there will be no noise as in real life and your classifier could be much less effective finally on real data. But usually to generate synthetic DB is fast, so why not to try.
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I was wondering whether and how it is possible to use a python generator as data input to scikit-learn classifier's .fit() functions? Due to huge amounts of data, this seems to make sense to me.
In particular I am about to implement a random forest approach.
Regards
K
The answer is "no". To do out of core learning with random forests, you should
Split your data into reasonably-sized batches (restricted by the amount of RAM you have; bigger is better);
train separate random forests;
append all the underlying trees together in the estimators_ member of one of the trees (untested):
for i in xrange(1, len(forests)):
forests[0].estimators_.extend(forests[i].estimators_)`
(Yes, this is hacky, but no solution to this problem has been found yet. Note that with very large datasets, it might pay to just sample a number training examples that fits in the RAM of a big machine instead of training on all of it. Another option is to switch to linear models with SGD, those implement a partial_fit method, but obviously they're limited in the kind of functions they can learn.)
The short answer is "No, you can't". Classical Random Forest classifier is not an incremental or online classifier, so you can't discard training data while learning, and have to provide all the dataset at once.
Due to popularity of RF in machine learning (not least because of the good prediction results for some interesting cases), there are some attempts to implement online variation of Random Forest, but to my knowledge those are not yet implemented in any python ML package.
See Amir Saffari's page for such an approach (not Python).