I want to extract Audio Features using RBM (Restricted Boltzmann Machine). For this, I am giving the spectrogram (PCA whitened) as an input to the RBM. For each audio file, The spectrogram is a matrix with no. of columns fixed but with different number of rows for each audio file. My question how can I train my RBM, or how can I extract the features from audio using RBM, given this spectrogram matrix. I read in a paper by Honglak Lee, paper title Unsupervised Feature Learning for Audio Classification using convolutional deep belief networks. http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2009_1171.pdf "We then trained 300 first layer bases with a filter length of 6 and a max-pooling ratio of 3." First, what is meant by bases here. (They have used Convolutional Deep Belief Networks, so I guess, bases do not mean weights here). Second, what do they mean by using a filter length of 6? How can I do it? Any hint will be appreciated. (I am new to RBM)

  • $\begingroup$ It depends on the purpose of your features. What will you use them for (speaker recognition, artist recognition, speech recognition and so on). $\endgroup$ – Nikolay Shmyrev Dec 11 '13 at 0:36
  • $\begingroup$ I am using it for speaker recognition (say). The goal later on is to use it for speaker recognition, and speech recognition as well. $\endgroup$ – user35919 Dec 11 '13 at 17:36

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