Hello I am trying to do sound classification in matlab. I have different samples of sounds for 2 seconds. How can I proceed with that. The sounds I am using are churchbell, footsteps, trains, sirens and people talking.
Your data set determines how to proceed.
If you have many ( thousands ) labeled examples of each class, and a fixed set of classes, a black box convolutional neural net with many layers is a straightforward solution.
At the (an)other extreme, you have only a few examples of each class, and have reason to expect the number of classes will grow in the future, a “this class” and everything else is in the other class approach would make sense, so each class is treated separately. As a hypothetical example, you mentioned church bells, so you can look up articles on the acoustics of church bells and engineer a church bell feature set, specific to that class.
Another extreme, you have a few examples of each class, form a matrix with data, and cross your fingers and perform a singular value decomposition SVD, and it might resolve an orthogonal and compact set of projections.
A typical approach would be to
Usually, it is important to analyze the classification performance. In order to do so, it is useful to split the available data into a training and a test data set. Then you can determine one or more measures to evaluate the classification performance.
A number of questions here on DSP.SE deal with this subject. Some examples are