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 Read more…
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, Read more…
This week I finished the Google speech to text pipeline and a test script to demo my best network. I’m working on increasing my accuracy past 65%. I’m expecting to hit 70% accuracy next week. Additionally, I will work on creating a better dataset to train my final network on. Read more…
This week I trained finished training a few networks. The highest accuracy I achieved was 53% which is less than ideal. I trained on 40,000 tweets scraped from twitter and did some minimal preprocessing. Going forward, I want to implement the Google speech to text pipeline and train the network Read more…
With the transition to online classes, I will no longer be building and designing the physical product for our project. Additionally, I will no longer need to manage porting code to the Raspberry PI or worry about its limited capabilities. The core of my workload going forward will consist of Read more…
This week we mainly planned on how to reform our project in order to work on it remotely. I was formerly responsible for designing and building the physical product. We have decided that is not viable to pursue this goal. Additionally, we are no longer using a Raspberry PI to Read more…
This week I mostly worked on our design report. I also worked on setting up google speech to text and using sklearn’s word vectorizer. With two emotions, I was able to achieve around 80% accuracy with the word vectorizer. Going forward, I’m planning to work on the LSTM-CNN for textual Read more…
This week I focused on finishing the design review presentation and report. I worked on the text emotion recognition portion of the design review and helped design our slide deck. I also incorporated the feedback from the presentations and solidified my architecture for the text sentiment analysis. No matter which model Read more…
I started to work on the text-based emotional analysis. For the first iteration, I’m using Term Frequency-Inverse Document Frequency. This parameter gives the relative importance of a term in the data and is a measure of how frequently and rarely it appears in the text. It’s how we turn words into numbers. Read more…
I’ve finished setting up Python/OpenCV on my local machine and polished our block diagrams. I’ve also acquired the necessary data from the AffectNet and Cohn-Kahnade databases. The AffectNet database provides around 500,000 (~120gb of data) faces categorized into 11 emotions: The Cohn-Kahnade database is significantly smaller and also dated. Thus, Read more…