How to classify continuous audio

问题:

I have a audio data set and each of them has different length. There are some events in these audios, that I want to train and test but these events are placed randomly, plus the lengths are different, it is really hard to build a machine learning system with using that dataset. I thought fixing a default size of length and build a multilayer NN however, the length's of events are also different. Then I thought about using CNN, like it is used to recognise patterns or multiple humans on an image. The problem for that one is I am really struggling when I try to understand the audio file.

So, my questions, Is there anyone who can give me some tips about building a machine learning system that classifies different types of defined events with training itself on a dataset that has these events randomly(1 data contains more than 1 events and they are different from each other.) and each of them has different lenghts?

I will be so appreciated if anyone helps.

回答1:

First, you need to annotate your events in the sound streams, i.e. specify bounds and labels for them.

Then, convert your sounds into sequences of feature vectors using signal framing. Typical choices are MFCCs or log-mel filtebank features (the latter corresponds to a spectrogram of a sound). Having done this, you will convert your sounds into sequences of fixed-size feature vectors that can be fed into a classifier. See this. for better explanation.

Since typical sounds have a longer duration than an analysis frame, you probably need to stack several contiguous feature vectors using sliding window and use these stacked frames as input to your NN.

Now you have a) input data and b) annotations for each window of analysis. So, you can try to train a DNN or a CNN or a RNN to predict a sound class for each window. This task is known as spotting. I suggest you to read Sainath, T. N., & Parada, C. (2015). Convolutional Neural Networks for Small-footprint Keyword Spotting. In Proceedings INTERSPEECH (pp. 1478–1482) and to follow its references for more details.

回答2:

You can use a recurrent neural network (RNN).

https://www.tensorflow.org/versions/r0.12/tutorials/recurrent/index.html

The input data is a sequence and you can put a label in every sample of the time series.

For example a LSTM (a kind of RNN) is available in libraries like tensorflow.