Posted on

Lin’s Status Report for 2/24

This week, since I’m the presenter of my group, I prepared for the design review presentation on Wednesday. I did a lot of practice to make sure I don’t need to look at notes during the presentation. Aside from the presentation, I researched on how the rhythm processor could work since my previous work mainly focused on pitch processing. After reading some past research and projects, I decided to use a window size of 1/8th of a beat. 

I also looked into Librosa to see how to find peaks for an input audio of 1/8th of a beat. I didn’t find the function but I found there’s a find_peak function in the Scipy library, so I decided to use that. However, the code still has some bugs right now and can not detect peaks correctly. 

I am slightly behind the schedule this week since I have both the design presentation and midterm paper due. I will definitely work harder next week to keep up with the schedule. I want to finish debugging my code by next week and test with some input audios generated from a real-life saxophone.

Posted on

Lin’s Status Report for 2/17

This week my main focus is to work on the pitch detection of the audio processing. I have transferred all my work from Matlab to Python, since it will be the primary language of our Web App’s backend. Currently, I am applying Short-time Fourier transform(STFT) to an input audio to get its frequency. Then I convert the pitch into MIDI notes and pair them with music notes. I tried several python libraries including Librosa and Scipy, which all deal with music and audio analysis. For now I’ve decided to use Librosa mainly since it’s been widely recommended by the StackOverflow users, but if things doesn’t work out later I’ll switch to Scipy.  I’m able to extract pitch and note from a 12tet diatonic music scale as shown in the graph below. 

My progress is on track this week. I wrote my code for music files within 10s and I plan to work on musics that are longer next week. Our design goal is to perform pitch detection to a roughly 60s music file so I will start to work on that next week. I will also try with some low SNR input files to test the pre-processing part of my code.

Posted on

Team Status Report for 2/10

Our team’s major concern for now is that parts may arrive later than expected. If the sensors and saxophone parts don’t arrive by next week, we will make some slight changes to our schedule by letting Jordan shift to helping the WebApp construction. Other than that there’s little risk since our goal this week is to get a clear view of our project and get things started.

We also updated our block diagram to include what our WebApp feedback will look like. We want to separate the feedback users may get from the WebApp into two major types, each has its own displaying. We will discuss more about what specifically “potential solutions” refer to next week.

Overall, we are in good track of our progress this week and every one has started to do their own job. The main design of our project remains the same, with some possible slight changes on labor division.

Posted on

Jordan’s Status Report for 2/10

This week, I mainly spent time preparing my proposal presentation on Wednesday. After the report, I explored ways I could acquire a saxophone, including from various organisations on campus, and buying a new one. I am still in communications with Kiltie regarding borrowing one of theirs. My goal for next week is to get the saxophone and place all orders for the hardware part by Tuesday, and spend the rest of the time on finalising the features and feedback that will be included in the web app. As the saxophone player in the team, this is naturally my job, and while I am still waiting on parts, this is the best way to spend my time. Overall, I am still on schedule.

Posted on

Lin’s Status Report for 2/10

This week’s main goal is to finish the proposal presentation and get ourselves familiarize to our sub-parts. Since I am in charge of developing the audio processor, I have started to research on the essential steps for it. The audio processing will be divided into pre-processing and pitch detection. I started working on the pre-processing part on Matlab and tested several filtering algorithms on an input audio.  I also researched on several python libraries for music processing, such as Librosa, SciPy, etc.

My progress is currently on schedule, but I hope to achieve more next week. I plan to finish up the pre-processing of audio next week and convert it into Python (if the webapp frontend can be constructed by then). I will also start to research on pitch detection next week.