## Inspiration

Our inspiration stemmed from the desire to implement a machine learning / A.I. API.

## What it does

Taper analyzes images using IBM's Watson API and our custom classifiers. This data is used to query the USDA food database and return nutritional facts about the product.

## How we built it

Using android Studio and associated libraries we created the UI in the form of an Android App. To improve Watson's image recognition we created our custom classifier and to recognize specific product brands.

## Challenges we ran into

For most of us this was our first time using both Android Studios and Watson so there was a steep initial learning curve. Additionally we attempted to use Microsoft Azure along side Watson but were unsuccessful.

## Accomplishments that we're proud of

-Successful integrating Watson API into a Android App.
-Training our own visual recognition classifier using python and bash scripts.
-Retrieving a products nutritional information based on data from visual recognition.

## What we learned

We experience and learned the difficulty of product integration. As well, we learned how to better consume API's

## What's next for taper

-Creating a cleaner UI
-Text analysis of nutritional data
-day to day nutrition tracking
## Inspiration

We hope this app encourages users to invest in the stocks and grow their personal investments.

## What it does

When the user sees a logo in everyday life and wants to learn more about the financial performance of said company in the stock exchange, the user can simply perform the airtap gesture and the HoloLens will take a snapshot of the current view. Then, the image is sent to Google’s Cloud Vision API and analyzed to see if there are any logos in the picture. If a logo is detected, the company of the logo is found and the NASDAQ API is used to determine the recent performance of the company’s stock. Finally, this financial data is visualized on the HoloLens through Unity.

## How we built it

We had the HoloLens take a picture after receiving an airtap gesture. The picture is then run through a chain of APIs, specifically Google Vision (to detect logos), a company name .csv (to get ticker names from company names), and NASDAQ API (to check stock prices).

## Challenges we ran into

We had challenges in implementing a gesture to capture photos, capturing photos, sending a JSON request through Unity to the Cloud Vision API, parsing XML from the NASDAQ API, and scripting in Unity. Because the HoloLens hasn’t been developed on much yet, there was little documentation and examples to learn from.

## Accomplishments that we're proud of

Becoming familiarized with developing for an AR environment, using Unity and the Windows Holographic Platform. Figuring out the Cloud Vision image recognition API to detect images from the snapshot the user takes on the HoloLens.

## What we learned

AR/Mixed Reality is an emerging field and devices like the HoloLens have incredible potential. It was a great learning experience to work with the HoloLens and figure out how to use Unity

## What's next for Logo Lens

1) By incorporating Capital One API, we could simulate buying and selling stocks on the go.
2) Selecting what pieces of data are more helpful to the user and presenting them using clean and easy to understand visualizations.
## Inspiration

One of the members of our team, Clinton, suffers from a stutter. While discussing possible novel uses of speech APIs, he mentioned that people with stutters often have verbal blocks, which prevent them from speaking normally. People with stutters have to learn how to change their speech in order to avoid problematic phonetic structures (using a technique known as word substitution). We realized that it would be valuable to develop a system that helps people with stutters identify sources of verbal blocks as well as ways to improve their speaking.

## What it does

Our product gives a profile on stress points and places for improvement when it comes to stuttering. Given an audio file, it detects stuttering and discovers the words or sounds that consistently cause issues.

Then, combining our stuttering analytics with the rich word data extracted from the Rev AI transcription, we produce a speech report that gives several helpful metrics:

* a graph of the types of phonemes that users have difficulty with (which is useful for voice training and speech therapy)
* the most problematic phonemes, ranked by stutter frequency
* word replacement recommendations, to help users develop their word substitution technique

## How we built it

Using a machine learning algorithm, we built a system that identifies when a person stutters, as well as the phoneme that causes them to stutter. Then, using Rev's speech-to-text API, we can triangulate the precise word that a person stuttered on. The API allows us to index transcribed speech by speaker, time, and consonant/vowel sound, and so we can document which words and sounds are most problematic for our users.

Then, combining our stuttering analytics with the rich word data extracted from the Rev AI transcription, we collect useful metrics, which we have visualized in our presentation.

## Challenges we ran into

Finding a rich corpus of audio data to train the SVC algorithm on was extremely difficult and laborious! As one might expect, training data of subjects demonstrating stuttered speech isn't easy to come by. To make matters more difficult, the data we found wasn't preprocessed, which meant we had to listen to and manually label every one-second instance in which we identified stuttering—prolongations and repetitions.

## What we learned

Programming is hard.

More seriously, we became very familiar with the intricacies of planning and executing a larger-scale project. There are often non-trivial details that get overlooked until they become the biggest challenge of the entire project, and this experience improved our ability to foresee such issues and solve them.