1. Modeling Machine Sounds, and Detecting Mechanical Failure Albert Wu Speech & Audio Processing March 9 th, 2006

2. Idea The motivation behind my project is this: machines convey lots of information about their state within the sounds they produce, which sometimes isn’t immediately obvious to human listeners. The idea is that most of us can recognize the sounds of machine failure events: breaking, collisions, engines or fans overworking. So it seems like a computer should be able to do better (ie, there is probably more info in the sounds that we don’t hear). The goals of this project are to a) develop a better understanding of machine/mechanical sound events b) listen in on these hidden modes c) to detect these events in the presence of a noisy environment.

3. Modeling machine sound? It’s useful to create models for machine sounds, so we have some sort of ground truth. Although the models I create may not be the most robust, I’ll try to start simply and build up in complexity. For now, the machine failure event sounds that I’ve been focusing on have been impact collisions and grinding sounds. I’ve been experimenting with the models in Gaver’s paper ‘Synthesizing Auditory Icons’, which we covered in class a few weeks ago.

4. Gaver’s Description of Impact Parameters

5. Simple Implementation of Gaver’s Description of an Impact Gaver models an impact as the sum of exponentials of various amplitudes and decays. As I showed on the previous slide, parameters can model different impact attributes. Impact.m

6. Statistical Analysis Drawbacks: a large amount of machine sound data is required for a failure-detection system, and this data is costly. But a physical modeling approach would allow us to create our own data; furthermore, this data would be parameterizable. Of course, no physical model we could ever produce could ever match an actual recording of the machine sound. If we could create our own data, based upon the simple physical models I create, this might help alleviate the problem.