Is This the Future of Music? The AI Startup Challenging the Boundaries of Song Transcription

Songscription, an emerging startup launched recently, aims to revolutionize music transcription with sophisticated AI technology that swiftly converts audio recordings into precise sheet music. Catering to both professional musicians and hobbyists alike, the company follows a freemium subscription model.

According to CEO Andrew Carlins, a Stanford MBA/MA student, Songscription’s vision is to simplify and enrich the creative process for all musicians. He envisions scenarios such as a rural high school music teacher utilizing the service to obtain accurate, tailored sheet music for the songs their students wish to perform. In such an example, the AI-generated arrangements would be customized specifically for the available instruments and the individual skill levels of the performers.

Currently, the software can reliably produce sheet music for several instruments, with piano transcriptions being the most accurate. In future versions, Songscription plans to expand its transcription capabilities, offering outputs like guitar tablatures and full band arrangements. Additionally, users who may not have formal training in music reading can generate visual piano rolls, digital representations of how notes are played on a virtual keyboard.

One notable feature is Songscription’s ability to directly produce sheet music from YouTube links. While users must affirm they possess the rights to transcribe uploaded content, enforcing these conditions may prove challenging, as this process could potentially allow users to bypass copyright restrictions.

Regarding legalities surrounding creative AI applications, Carlins recognizes certain gray areas exist. While it is generally permissible for individuals to listen and manually transcribe music for personal use or rehearsal, the legality becomes less clear when technology is employed to automate these steps. Carlins acknowledged that such scenarios represent evolving legal ground and emphasized that Songscription sees itself as complementing musicians’ efforts rather than offering unauthorized access to musical content.

The underpinning technology powering Songscription originated from research by co-founder Tim Beyer, co-authored with researcher Angela Dai. To train their AI models, the team gathered a mix of musician-contributed performances and publicly available sheet music alongside synthetically generated data. For synthetic training data, the startup created computer-based audio renditions of music scores, subsequently modifying them with realistic background noise and acoustical effects to simulate natural conditions.

Within seven months since its founding, Songscription has attracted initial funding in a pre-seed round backed by Reach Capital and is set to participate in Stanford’s StartX accelerator program.

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