I think we all need more information. What are the features you are extracting ? What is the label you are trying to predict ? Type of music ? Singer ?
If KNN and RFs do not perform better, maybe the predictors are not relevant with respect to the target.
Besides, you did not mention parameter tuning, which has an important impact on the performances of the SVM.
Edit.
As you seem to be performing mutliclass prediction, 50% is not that bad (it would be awful in the case of a binary classification).
Now, if you want to improve the performance of the model, you could try various kernels for your SVM and change their hyper-parameters. The cost parameter C of SVMs plays an important role as well.
If the performance reached with your model after parameter tuning is not satisfying, you should try to add more features. Not being an expert in sound classification, I cannot help you.