Team Status Report for 04/25/2026

The remaining risks are primarily related to presentation quality and clearly communicating our results in the final poster and report. We are managing this by organizing our data, refining how we present metrics, and making sure our results clearly map to our requirements. Since the system itself is stable and validated, there is minimal need for contingency planning beyond ensuring all results and visuals are properly documented. If any issues arise, we can fall back on our tested data and MVP demonstration.

No major changes have been made to the system requirements, block diagram, or overall system specification. The team has remained consistent with the original design and focused on executing testing and validating performance. Any minor adjustments made were related to testing procedures and measurement methods to ensure accurate and reliable results.

Our unit tests included steering error measurement using a protractor-based 2D setup, interferer suppression testing by comparing input/output dB levels at a 60° offset, and latency testing using input-to-output timing measurements. Our system-level tests included target lock accuracy testing through repeated human trials and overall integration testing of the full pipeline. Based on results, the system meets all major requirements, including target lock accuracy (91.4%), steering error (12.3° AAE), interferer suppression (8.7 dB), latency (84.9 ms), cost (~$447), and setup time (1.5 minutes). Intelligibility testing was completed separately and confirmed that the system meets the ≥20% ASR improvement requirement.

No major design changes were required, as the system architecture proved effective. The improvements were focused on refining testing methods, ensuring accurate measurement, and stabilizing system performance. Overall, experimentation validated that the system meets its engineering requirements and performs effectively for its intended use case.

Nasir’s Status Report for 04/25/2026

This week I completed intelligibility testing for the Focus Field system using our defined speech-to-text (STT) comparison method. I recorded baseline audio using a standard headphone microphone and compared it against audio processed through our system, then ran both through the same STT tool. I measured performance using word-level error metrics (missed words, extra words, and substitutions) and computed the overall error rate and relative improvement. Based on these results, our system meets the ≥20% ASR error reduction requirement.

At this point, we have completed testing across all major system requirements and have validated that the system meets our design goals. This week I plan to finalize contributions to the project poster and complete my sections of the final report. I also want to ensure that all testing results are clearly documented and presented in a way that supports our design requirements and overall system performance.

Nasir’s Status Report for 04/18/2026

This week I focused on system testing and was able to fully complete testing for steering error, latency, target lock accuracy, and interferer suppression. I ran structured trials and verified that most of our system requirements are being met, with the exception of latency, which is currently slightly above the ≤100 ms target (currently at 104ms). I also spent time refining and debugging the testing process itself. Overall, this week was focused on collecting reliable performance data and ensuring that our testing methods are accurate and meaningful.

My progress is on schedule in terms of completing testing and validating system performance, but slightly behind on meeting the latency requirement. The next step is to optimize the system pipeline and reduce unnecessary processing delays. I am in a good position to quickly iterate and improve performance.

Next week I plan to run intelligibility testing. This will allow us to complete testing across all major system requirements. I also plan to continue refining system performance, particularly focusing on reducing latency to meet the ≤100 ms target.

Throughout this process, I had to learn more about system-level testing, especially how to correctly measure and interpret performance metrics like latency. One key realization was that designing a test is just as important as running it, since incorrect measurement methods can lead to misleading results. To learn these concepts, I relied on the strategy of troubleshooting through trial and error. This hands-on approach, combined with iterative debugging, helped me quickly identify mistakes and improve both my testing methods and overall understanding.

 

Nasir’s Status Report for 04/04/2026

This week I focused on running system tests and continuing development of our testing infrastructure for the Focus Field project. I conducted 67 human trials for target lock accuracy with 1–4 users and recorded results, achieving a 77.6% success rate (52/67), which shows the system is functional but still needs improvement. I also performed steering error testing by physically measuring angular offsets using a protractor in a 2D horizontal plane at 1 meter, aligning with our intended use case. In addition, I continued working on our testing suite, including debugging and fixing an issue in one of the automated test files. I also prepared the system for upcoming tests, including interferer suppression and latency, which already have implemented testing scripts.

My progress is on schedule in terms of development and testing, but slightly behind in meeting performance targets, specifically target lock accuracy. However, this is expected during the testing phase as we begin identifying system limitations. Moving forward, I plan to use the collected data to refine the system, focusing on improving accuracy through better alignment and potential algorithm adjustments.

Next week I plan to run interferer suppression and latency tests using the testing suites that are already implemented. I also want to begin intelligibility testing using our defined speech-to-text comparison approach. The goal is to collect full performance data across all requirements and continue improving the system based on those results.

At this stage, I have focused on verifying that individual components of the system meet their defined requirements through structured testing. For target lock accuracy, I verified system performance by running 67 trials and comparing the measured success rate (77.6%) directly against the ≥90% requirement. For steering error, I verified the system’s directional accuracy by measuring angular deviation using a protractor-based setup in a 2D horizontal plane at 1 meter and comparing results to the ≤15° threshold. I have also prepared verification tests for interferer suppression and latency, including implementing and debugging the corresponding testing scripts, although full results have not yet been collected. Additionally, I have defined the verification approach for intelligibility using ASR error comparison, which will quantify performance once executed. Overall, my verification work ensures that each subsystem can be quantitatively evaluated against its specific engineering requirements.

Team Status Report for 04/04/2026

At this stage, the main risks are improving target lock accuracy to meet our ≥90% requirement and finalizing a stable chassis structure for consistent hardware alignment. While we currently have a viable MVP that demonstrates core functionality, performance tuning and physical stability are the key areas that could impact final success. We are managing these risks by continuing structured testing, analyzing results, and iterating on both the algorithm and physical setup. The chassis is being refined through iterative design to ensure proper camera and microphone alignment.

No major changes have been made to the overall system requirements or architecture. However, we are refining aspects of the physical design, specifically the chassis, to improve stability and alignment, which directly affects system performance. These adjustments are necessary to ensure consistent and reliable testing results.

For validation, we are focused on demonstrating that the system meets its intended use case of improving speech clarity in multi-speaker environments. We have testing suites ready for interferer suppression and latency and have resolved initial bugs, with testing to be executed next. Intelligibility testing is also planned using speech-to-text comparisons to measure ASR error reduction. Moving forward, we will analyze results by comparing measured performance directly against our defined thresholds and iterating on both system design and hardware setup. Overall, while some metrics like target lock accuracy still need improvement, we have a solid MVP and a clear path to validating the system against our requirements.

Nasir’s Status Report for 03/28/2026

This week I focused on system testing for our Focus Field prototype, specifically evaluating our core performance requirements using the fully assembled setup. For target lock accuracy, I conducted 67 human trials with 1–4 users in view and recorded system performance, resulting in a 77.6% success rate (52/67), which is currently below our ≥90% requirement. I also ran steering error tests by physically measuring angular offsets using a protractor on a flat plane at 1 meter, aligning with our system assumption of horizontal-only (2D) measurement. These tests helped validate our testing methodology while also identifying areas where system performance needs improvement. In addition, I continued working with our testing infrastructure and debugging, including identifying and fixing an issue in one of our test files related to automated evaluation. Overall, this week was focused on collecting real performance data and validating our testing pipeline.

Progress may be slightly behind in terms of meeting performance requirements, particularly with target lock accuracy currently below our threshold. However, this is expected at this stage since we are now transitioning from setup into real testing and identifying system limitations. The next step is to analyze the causes of the lower accuracy and refine our approach, whether through improved alignment, algorithm adjustments, or system tuning. Since our testing framework is now working, we are in a good position to iterate quickly and improve results.

Next week I plan to run additional system tests for interferer suppression and latency using the testing suites that have already been implemented. I also plan to begin intelligibility testing using our defined approach with speech-to-text comparison to measure ASR error reduction. The goal is to collect complete performance data across all major requirements and continue refining the system based on those results.

Team Status Report for 03/21/2026

The most significant risk right now is not having the full physical system assembled and stable in time for the interim demo, mainly because the chassis and 3D printing have taken longer than expected. Since the cameras and microphone array depend on the chassis for secure mounting and alignment, delays in this step affect our ability to fully demonstrate the integrated system. We are managing this risk by keeping the team focused on interim demo priorities, preparing all other subsystems in parallel, and planning to move quickly into final assembly and testing as soon as the chassis is ready. Our contingency plan is to present the most complete version of the system possible, even if the full physical integration is not finalized, by demonstrating subsystems, test infrastructure, and the progress already made toward the final prototype.

At this time, no major changes have been made to the core system requirements, block diagram, or overall system specification. The team’s effort has been concentrated on preparing for the interim demo and completing implementation rather than revising the design itself. Any adjustments made recently have been limited to practical integration details, particularly around the chassis and mounting approach, in order to ensure the hardware can be assembled securely and demonstrated effectively. These adjustments do not create major additional cost beyond minor time and iteration, and we plan to mitigate those costs by finalizing the physical design as efficiently as possible so the team can return focus to testing and validation.

Nasir’s Status Report for 03/21/2026

This week I spent my project time preparing for our interim demo and helping the team work through the final blocker preventing full system assembly. A major focus was the chassis, since the 3D printing process has taken longer than expected and this has delayed mounting and aligning key hardware components. I stayed involved in discussions around the chassis design, fit, and readiness so that once the printed parts are complete, we can move quickly into final assembly and demo preparation. I have remained actively engaged in coordination, troubleshooting, and preparation work to keep the project moving forward.

Our progress is slightly behind because the chassis has taken longer to complete than expected, and this is currently the last major blocker before we are ready for the interim demo. To catch up, we plan to move immediately into assembly, alignment, and confirmation testing as soon as the printed chassis is available.

Next week I hope to help complete final chassis integration, mount the cameras and microphone array onto the system, and support the team in getting the full prototype ready for the interim demo. I also want to begin or continue confirmation testing once the hardware is assembled so we can validate that the system is operating as expected. The main deliverable is a working, demo-ready prototype that shows the core functionality of Focus Field.

Nasir’s Status Report for 03/14/2026

This week I spent most of my time working on the ethics assignment. While completing the assignment, I revisited our system requirements and design decisions to make sure they were aligned with ethical considerations such as user privacy, accessibility, and responsible deployment. This review helped confirm that many of our requirements such as accuracy thresholds, latency limits, and cost constraints; support broader societal goals like accessibility and equitable access to communication technology.

In addition to the ethics work, I continued developing the digital testing suite for the project. I refined portions of the code related to our automated testing infrastructure so that the system can more reliably measure performance metrics as we begin integrating hardware. This included improving parts of the testing logic and making sure the framework can properly capture, log, and compare relevant metrics when the system is running. The goal of this is to ensure that once the full system is operational, we have a consistent and repeatable way to validate whether our design meets its defined requirements.

My progress is still on schedule. We will finally be able to test once we get the system fully mounted on its chassis.

Next week I plan to continue working on the testing suite and begin verifying that the existing tests operate correctly when the system is running in real time.

Team Status Report for 03/07/2026

One of the most significant risks at this stage of the project is designing a chassis that securely mounts the cameras while maintaining proper alignment with the microphone array. Because the system relies on visual detection and directional audio working together, even small shifts in camera positioning or misalignment between the cameras and microphones could negatively affect speaker identification accuracy and steering performance. Ensuring the physical stability and precision of the hardware layout is therefore critical to achieving reliable system behavior during testing and operation.

To manage this risk, the team plans to carefully evaluate multiple design options for the device’s chassis before finalizing a solution. We will use an iterative prototyping approach, gradually refining the physical design to ensure the cameras remain stable and correctly aligned with the microphone array. This process will involve testing different mounting approaches and making adjustments based on observed performance and stability.

At this time, no contingency plan is necessary because the risk is manageable through design iteration and careful prototyping. No changes have been made to the system requirements, block diagram, or overall system specifications in the past couple of weeks.

A was written by Cole Franklin, B was written by Nasir Crownie and C was written by Omar Ghabayen. Nasir reviewed and edited for unity.

A. Global Factors

The Focus Field project considers global factors by addressing a communication challenge that affects people in many different regions and work environments around the world. As remote work, virtual meetings, and online collaboration become more common globally, many people participate in calls from noisy homes, shared offices, or public spaces. By designing a low-cost system that improves speech clarity without requiring specialized infrastructure, the project aims to support clearer communication for users across different countries, professions, and levels of technical experience.

B. Cultural Factors

The project also considers cultural factors by recognizing that communication technologies should support speakers of many different languages and backgrounds. People participate in meetings, classes, and collaborations using a wide variety of languages and accents, so the system is designed to improve speech clarity without relying on language-specific processing. By focusing on isolating the active speaker’s voice rather than interpreting the language itself, the device can support clearer communication for users regardless of the language they speak, helping make the technology more inclusive and accessible across diverse cultural communities.

C. Environmental Factors

The Focus Field project has minimal environmental impact because it is designed as a small, USB-powered desktop device. It does not require large infrastructure, high energy consumption, or specialized installations, and it operates using standard computing hardware that is already common in many work environments. As a result, the system does not significantly affect natural resources or the surrounding environment beyond typical low-power electronic device usage.