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Yoojin’s Status Report for 4/26/20
Trained 4 models, male/female positive/negative emotion classification models and male/female multi-emotion classification models multi-emotion classification models perform poorly, so will be using binary classification instead Combined text and tone analysis Moved nets to cloud (but still need to work on getting it hooked up with the web app) Web app hooked up to ec2 instance
Vinay's Status Report
Vinay’s Status Report for 4/26/20
This week I focused on completing integration and preparing for the final presentation. Last week I was struggling with how to divide the journal entries into sentences. Google Speech Recognition works best on 5 second snippets of audio. Additionally, the library is not able to recognize sentence boundaries. My solution was to record the entire journal entry and split it into sentences based on audio silence.This approximates of sentence boundaries worked well for speakers with Read more…
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Team Status Report for 4/18/20
Redeemed our cloud credits Planned out integration Work on: Actually integrating all the components Making video Sharing code
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Yoojin’s Status report for 4/18/20
Training the voice tone analyzer Keep working on: Integrating the team’s parts together! Put everything on Github to share with team members Put everything on cloud
Vinay's Status Report
Vinay’s Status Report for 4/18/20
I’ve reached 60% overall accuracy for the 7 categories (6 emotions + neutral). I’ve started to work on integration. I’m able to read speech from the computer mic and analyze it using python’s SpeechRecognition library. Right now, the program listens to a user until it hears a second of silence, then it closes the stream. Additionally, the entire journal entry is recorded as one long sentence with no punctuation. I need to figure out how Read more…
Patrick's Status Reports
Patrick’s Status Report for 4/18/20
Completed: Set up AWS instance Processed 2058 of each emotion and trained Accuracy around 50 to 60% Used log loss for SVM Todo: Balance emotions in train/test split Currently shuffled split may not be balanced Migrate code to AWS and expand EBS storage Train second classifier for contempt, disgust, and fear
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Team Status Report for 4/11/20
This week our group continued to work on our individual modules. Patrick trained around 60000 images for his facial emotion recognition and will add a secondary SVM for detecting emotions that are harder to tell apart. Vinay finished his first iteration of training for textual sentiment analysis. Yoojin added video streaming to the web app and is working on a speech tone emotion network. Going forward, we hope to finalize our networks and integrate them Read more…
Patrick's Status Reports
Patrick’s Status Report for 4/11/20
Completed: Trained with 60000 images Balanced out images of each emotion Confusion matrix Accuracy of around 60-70 percent 7000 images – got around 80-90 percent but could be overfit Tested on my own face – contempt, anger, disgust, fear are confused Todo: Make second SVM for classifying hard to differentiate emotions Continue to up accuracy Train with more images Cross-validation?