Simile was a final project for 05-317, The Design of Artificial Intelligence Products, in the Human-Computer Interaction Institute at Carnegie Mellon University. Students were tasked to envision a novel product or service that employs natural language processing (NLP) technology. My team created an NLP lyric analysis tool to help aspiring musicians and songwriters craft and revise their song lyrics.
Students were tasked to envision a novel product or service that employs natural language processing (NLP) technology. Using a matchmaking process and a competitive analysis, we narrowed 20 initial ideas down to one value-focused NLP system for a specific set of target users. Ideation process includes finding and/or constructing an appropriate dataset(s) for inferences, accounting for inference errors, and prototyping to our greatest risk.
We sent a survey to musicians to gauge interest in a tool that helps with song writing.
Nearly every musician struggles with writing lyrics. If we can create a tool that doesn’t interfere with the creative process, but provides valuable analytics and recommendations when writers are stuck, they may be willing to pay for this kind of support.
Mark is a...
Mark wants to finish creating lyrics for a song but......
Using the One Million Songs dataset, we could create a comprehensive dataset for our NLP tool with difficult labels already generated. This makes the feasibility of our idea low to moderate.
Our primary difficulty for song analysis then becomes a classification task of labeling user-generated lyrics. Our generation tool can suggest lyrics other artists would use and detect emotion using common sentiment analysis and emotion detection algorithms.
Artist name, song name, lyrics, album, terms, genre, sections, loudness, tempo time signature, key, energy, danceability, segment timbre, duration, similar artists, start/end times for segments, etc.
We started by exploring what musicians might want to control in an interface.
However, we gradually gravitated towards focusing primarily on the writing itself, very similar to the correctional grammar tool, Grammarly. We wanted to provide artists with a clutter-free writing space with the NLP lyrical tool readily accessible. Our interface is collapsible, with multiple views.
Paid users can access additional, generative features which provide in-line suggestions for rhymes and words that suit their lyrical criteria with lyrical suggestions based on genre, emotion, and phonetic rhyming. For example, an artist could look for words similar to those Taylor Swift uses in her songs, words that rhyme, or other words frequently used in pop music in context of his or her song.
We received valuable feedback from musicians, peers, and instructors from surveying musicians and three in-class presentations, pivoting from the patent application process to songwriting. Feedback was positive and helped us refine a viable prototype for our greatest risk and challenge—adoption.
Musicians voiced interest in using a lyrical tool, but have various opinions on payment. Figuring out how to accommodate for the variety of responses and create a viable business model proved difficult. We subsequently addressed this ambiguity by creating a freemium model separating analytical from generative features of NLP.
Feedback from the class expressed concerns over how significantly NLP will improve the creative writing process from a simple rhyming dictionary, and what additional capabilities might be necessary to increase adoption. We used this to define other capabilities that would make such a tool convenient in more just lyric generation, and a roadmap for hooking in our target audience early on in their careers as budding musicians.
For information about other AI-based projects from this class, please contact me at email@example.com.