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- Creators: Computer Science and Engineering Program
Description
If you’ve ever found yourself uttering the words “Honey, I forgot the—” or “how did I miss the—" when coming home from the grocery store, then you’re not alone. This everyday problem that we disregard as part of life may not seem like much, but it is the driving force behind my honors thesis.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
Shopping Buddy is a complete Amazon Web Services solution to this problem which is so innate to the human condition. Utilizing Alexa to keep track of your pantry, this web application automates the daunting task of creating your shopping list, putting the power of the cloud at your fingertips while keeping your complete shopping list only a click away.
Say goodbye to the nights of spaghetti without the parmesan that you left on the store shelf or the strawberries that you forgot for the strawberry shortcake. With this application, you will no longer need to rely on your memory of what you think is in the back of your fridge nor that pesky shopping list that you always end up losing when you need it the most. Accessible from any web enabled device, Shopping Buddy has got your back through all your shopping adventures to come.
ContributorsMathews, Nicolle (Author) / Meuth, Ryan (Thesis director) / Chen, Yinong (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
This work focuses on combining multiple different technologies to produce a scalable, full-stack music generation and sharing application meant to be deployed to a cloud environment while keeping operating costs as low as possible. The key feature of this app is that it allows users to generate tracks from scratch by providing a text description, or customize existing tracks by supplying both an audio file and a track description. Users will be able to share these tracks with other users, via this app, so that they can collaborate with others and jumpstart their creative process, allowing creators to produce more content for their fans. A web app was developed; Contak. This application requires a database, REST API, object storage, music generation artificial intelligence models, and a web application (GUI) to interact with the user. In order to define the best music generation model, a small exploratory study was conducted to compare the quality of different music generation models, including MusicGen, MusicLM, and Riffusion. Results found that the MusicGen model, selected for this work, outperformed the competing models: MusicLM and Riffusion. This exploratory study includes rankings of the three models based on how well each one adhered to a text description of a track. The purpose was to test the hypothesis that MusicGen produces higher quality music that adheres to text descriptions better than other models because it encodes audio at a higher bit rate (32 kHz). While the web app generates high quality tracks with above average text adherence, the main limitation of this work is the response time needed to generate tracks from existing audio using the currently available backend infrastructure, as this can take up to 7 minutes to complete. In the future, this app can be deployed to a cloud environment with GPU acceleration to improve response times and throughput. Additionally, new methods of input besides text and audio input can be implemented using MIDI instructions and the Magenta music model, providing increased track generation precision for advanced music creators with MIDI experience.
ContributorsZamora, Michael (Author) / Chavez Echeagaray, Maria (Thesis director) / Prim, Tadi (Committee member) / Day, Kimberly (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12