Frictions between a rigid-nib pen and paper result in audible sounds that are correlated with the dynamics of writing. Such writing-sounds were previously used as a biometric identity to achieve writer authentication. This paper presents an alternative and supplement algorithm for sound-based handwriting authentication. Envelopes of writing-sounds estimated by the Hilbert transforms are found useful in differentiating topologically similar characters written by different individuals. A straightforward supervised neural network in conjunction with a purpose-designed pre-processor can be trained on examples to effectively differentiate patterns of writing-sounds and thus achieve writer authentication, providing a straightforward and potential alternative to existing methods.