jfrog wrote:
> Hello,
>
> I'm looking to meet an undergrad design requirement,
First to nitpick a little.
In the eyes of your school, what is the purpose of "an undergrad design
requirement"? Or to rephrase, how will it be graded?
The answer to above might/should (???

be modified by your personal
goal in satisfying "an undergrad design requirement" ;!
> which can be done
> with independent study. I'll graduate in December, so I have a lot of
> coursework under my belt. I'll also have all summer to research and
> prepare.
[snip info which does not relate to MY reply]
[smaller snips indicated by "..." ]
>
> I've also taken a required electronics course, ... , but
> I'd rather work on a project that is software based. ... If I needed any hardware
> aside from my PC, I'd want to keep the cost under $150 dollars.
>
> I think I'd probably prefer to do a design project focusing on a DSP
> topic.... like maybe something with audio? ...
>
> So, does anyone have any ideas for interesting things I might be able to
> design that are DSP related? ... I thought about designing some audio
> filters or something like that (I had the idea of using one person's voice
> as a reference ...
[ OK that last ... possibly should have been a *SNIP* ;]
This suggestion is an outgrowth of an personal interest in *SOME*
aspects of speech recognition that is decades old that I've not had
*simultaneous* occurrence of available time, expertise (personal or
otherwise) and motivation.
"A Modest Proposal"
1. Determine the formants of a particular person.
[Or preferably do it for a number of individuals]
2. For each set of formants, create a FIR filter to emphasize those
frequencies.
[personal "prejudice" suspects it might be beneficial to tweak gains
such that output at each frequency is similar.]
The above is neither difficult NOR *SIGNIFICANT*
[nor probably deserving of desired grade]
Suggested significant(???) experiment.
Record speech by individual(s)
Mix with experimenter selection of noise sources
Filter each with filter created above
Analyze results by any/all of
a. how accurately can human understand
b. how accurately can a specific speech recognition program [trained by
specific speaker in noise free environment] recognize the passages
I would love to see the results of this experiment.
I'm definitely not an expert, but I've got a gut feel this might
demonstrate that modern speech recognition is barking up ANTIQUE trees.