This week, I was mostly focused on the Interim Demo. Last week, I had the idea of using the STFT to separate the audio file into frequency bins to analyze through the intensity analysis. I thought this would be more efficient due to the variability of the number of bins. However, I was having some trouble implementing it.<\/p>\n
I spoke to Professor Sullivan regarding the approach, and he mentioned that it would be better to use bandpass filters and then run an intensity analysis on that. I ended up implementing that with scipy and then running the intensity analysis on the filtered wav files.
\nOn Wednesday, I had an odd bug where there would be no outputs during long stretches of time during the audio file. I was able to fix that by scaling the array, and then analyzing it. That issue is fixed.<\/p>\n
Timing wise, while the algorithm takes about six minutes to analyze a three minute audio file, I should be able to get this down to three minutes as per the user requirements through streamlining the code. I will not be doing this for this week because this will likely be a finishing touch after tuning and testing the algorithm.<\/p>\n
Currently, the beatmap seems to have an offset correlating to a song. George suggested adding a noise to the audio file whenever my algorithm detects a beat, so I should have that done by Wednesday so I can better understand whether there is an offset- or what exactly is happening.
\nI am fine on schedule. We have had to change the Gantt Chart which reflects our scheduling differences. The updated Gantt will be on this week’s team update.<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
This week, I was mostly focused on the Interim Demo. Last week, I had the idea of using the STFT to separate the audio file into frequency bins to analyze through the intensity analysis. I thought this would be more efficient due to the variability of the number of bins. However, I was having some … <\/p>\n