Mitchell’s Status Report for Feb. 20

This week my efforts were focused on research and slide preparation. On the research end, I looked into audio filtering methods, aws setup, and methods of testing. I looked into the ReSpeaker, PyAudio, Audacity, and SciPi libraries for methods of audio processing and to see what we could leverage. I also looked for research papers for processing the audio, microphone feedback, and user design. I also looked into methods of testing like stress testing to make our system more robust.

I believe that I am currently on schedule. Our project proposal slides are complete and our initial gantt chart has been created.

From our gantt chart, I will be starting to setup the AWS server and website next week as well as starting to prepare the design presentation.

 

 

Cambrea’s Status Report for Feb. 20

This week we worked on creating an outline document with our research.  I added my research about hardware, why we are using a raspberry pi, which respeaker we are using and which AWS server we should use.  I also added research about how to transmit audio data on the application layer to the AWS server and how to compress the audio for creating packets to send.

From this research document we created the slides and since I am doing the proposal presentation I have been reviewing what I will say.  I also talked about each slide with my group.

The most significant risks we could have at this point would be incorrectly laying out our work on the gantt, and not getting the timing right since we are just estimating how long each task will take us.  To mitigate this we will be updating the gantt chart as we figure out more about how long each piece of the project will take.

We have worked on designing the system for the most part this week so we don’t have any changes to report.  We also have just created the schedule this week which is linked in our team report.

Team Status Report for Feb. 20

This week our team worked on design and planning in order to prepare our project proposal. We researched our requirements and technology solutions, divided work, made presentation slides, and drew up a schedule in the form of a Gantt chart. There are a couple risks that arise from this. First, there’s the risk that, not understanding how much work some aspects of the project might entail, we divided work in an unbalanced way. Here, we just have to be flexible and prepared to change up the division of labor if such issues arise. Second, there’s the risk that our schedule is unrealistic and doesn’t match what will actually happen — but this is counteracted by the nature of the document as something that will be constantly changing over time.

Since we were creating our design this week, we can’t really say that it changed compared to previously; but our ideas were solidified and backed up by the research we did. Some of the requirements we outline in our proposal are different from those in our abstract because of this research. For example, in our abstract we highlighted a mouth-to-ear latency of one second, but after researching voice-over-IP user experience standards, we changed this value to 150ms. 

We’ve just finished drawing up our schedule. You can find it below. We’ll point out ways that it changes in subsequent weeks. 

 

Ellen’s Status Report for Feb. 20

This week my efforts were focused on research and on preparing slides for our project proposal. On the research side, I examined a bunch of the requirements we included in our abstract and went digging around the internet for papers and standards documents that could shed light on the specific measurements of a good user experience. This was easier to do for some requirements than others. Machine learning related papers usually focused more on what was possible to achieve with the technology rather than what a user might desire from the technology. But in the end our list of requirements was solidified.

I went on a separate research quest to find viable ML speech to text and speaker diarization solutions and the academic papers associated with the various solutions. Comparing solutions based on metrics reported in papers is an interesting problem; the datasets on which the performance measures are calculated are mostly all different, and there are different performance measures, too (for example, “forgiving” word error rate vs “full” word error rate on some datasets)! My task was basically to search for solutions that did “well” — I might need to evaluate them myself later when we have our hardware.

Currently, I’d say that I’m on-schedule in terms of progress. This comes from the fact that we just came up with our schedule this week! In this next week I’m working on getting an initial version of our speech-to-text up an running. In the end I want to have a module that’ll take in an audio file and output some text, running it through a different ML solution depending on a variable that’s set. Near the end of next week I will also start on the pre-processing for getting audio packets into the correct form to be passed into the speech-to-text module.