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Results tagged with machine-learning
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user 54096
Machine Learning is a subfield of computer science that draws on elements from algorithmic analysis, computational statistics, mathematics, optimization, etc. It is mainly concerned with the use of data to construct models that have high predictive/forecasting ability. Topics include modeling building, applications, theory, etc.
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Is it feasible to use decision tree algorithms for sensor fault detection?
You need to determine how to formulate your problem. I see it as having two aspects:
1. Detect an abnormal value (in temperature)
2. Determine whether abnormal value is due to sensor or system problem …
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vote
could not broadcast input array from shape (13,160) into shape (13) while using sklearn norm...
Your MFCCs are a time-series, 2d representation. scikit-learn transformers like StandardScaler only works with 1d data (plus one dimension for the individual samples).
So you need to implement the sta …
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Unsupervised anomaly detection - dataset with multiple users
For a strategy that treats each "group" (in your case a person) as independent, the most straightforward is indeed to create one model per group, each with its own set of parameters.
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Predicting time of event from multiple timeseries
This kind of problem can be modelled in two different ways, which I call time-of-event and time-to-event. The first is what you have tried out so far, and illustrated in your picture.
Time-of-event
Fe …
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Accepted
Gaussian Mixture Model performance with data outliers
Gaussian Mixture Models can struggle when there are outliers in the data - as the standard formulations will attempt to ensure that all datapoints are modeled by the Gaussian mixtures.
A common approa …
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vote
Machine learning in audio?
For general sound, I recommend Computational Analysis of Sound Scenes and Events. Music and speech are popular sub-fields of audio that have a lot of literature dedicated to them, even before machine …
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How to gather training data for simple voice commands?
To train (and evaluate) a classifier for fixed-vocabulary speech commands
you should build a curated dataset that:
Has a well-defined list of commands (the classes for the classifier)
Has enough sam …
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3
votes
Machine learning in audio?
There is now a youtube channel dedicated to Audio Machine Learning, The Sound of AI. There is also an associated Slack community for discussions.
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What are the audio features to best describe a music?
The features you have selected are a good starting point, but are still (with the exception of tempo) quite "low level" compared to what might be most relevant for music recommendation systems.
The Es …
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Which model to use for multiclass audio classification?
500 samples for 6 classes is not so much. You should put aside about 100 samples for validation and 100 for testing, leaving 300 samples for training.
I'm assuming that these drum loop are on the orde …
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I am trying to implement Isolation forest for anomaly detection but I am not able to underst...
According to this answer the range of output from scikit-learn IsolationForest decision_function is between -0.5 and 0.5, where smaller values mean more anomalous.
The predict function then applies a …
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Problem to classify multilabel dataset while using random forest algorithm
The error is because Scikit-learn RandomForestClassifier does not support multiple outputs with N-classes per output. For multi-output only binary classification is supported.
Instead train separate …
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0
votes
Accepted
Opencv kmeans predict equivalent
The OpenCV kmeans function is equivalent to fit(). It returns the cluster centers, and you can use this to implement your own predict()function. For a new sample, calculate the distance to each of the …
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Classify sensor data (multivariate time series) with Python's scikit-learn decision tree
For usage you need to flatten the 2D raw sensor data into 1D features. Below code demonstrates the basics.
What kind of feature engineering to apply for best predictive effect depends entirely on the …
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1
vote
Why is pre and post silence important when collecting speech data?
Silence is often useful when segmenting the raw data into suitably sized samples for the machine learning methods. It is practical to run recording for several minutes at a time, but often the input t …
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