musicclassification's Introduction
The music classification ipynb provides all the methods and implementation of the algorithm. The methods to specify the feature extraction are: --fourierfeatures -> extracts n number of features after fast furouir transform @params (data, n) where data is a [tuple ([song_samples], samping_rate)] -> list of outputs from librosa.load() and n is the number of features to extract. default = 1000 --mfccfeatures -> extracts mean of features after mfcc transform @params (data) where data is a [tuple ([song_samples], samping_rate)] -> list of outputs from librosa.load() --getBest_Features -> extracts mean, standard Deviation, skewness and kurtosis of features after mfcc transform @params (data) where data is a [tuple ([song_samples], samping_rate)] -> list of outputs from librosa.load() The methods to specify the algorithm are: -- genericML1 -> This method encapsulates random forest @params (tdf,ldf) where tdf is training features dataframe and ldf is training labels dataframe -- genericML2 -> this method encapsulates k-nearest Neighbour @params (tdf,ldf) where tdf is training features dataframe and ldf is training labels dataframe ExampleUsage: Feature Extraction: training_features = getBest_Features(data = data) Running the algorithm: myModel = genericML1(trainingData,trainingLabel) prediction: myModel.predict(testingData)
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