MyPart Inc.
Liraz Amir has a diverse range of work experience in the tech industry. Since April 2021, they have been working as a Senior Frontend Developer at MyPart Inc. Prior to that, they worked at Gimmonix from July 2018 to April 2021 as a Full Stack Developer. Liraz also has experience as a Lecturer at HackerU - האקריו המרכז ללימודי מחשבים והשמת עובדים בהייטק, where they taught from November 2016 to November 2020. Their earliest work experience was as a Software Developer at Bizwise Technologies Ltd from January 2015 to May 2018.
Liraz Amir's education history shows that in 2010, they did not attend any school and did not pursue any degree or field of study during that time.
MyPart Inc.
MyPart is an AI-powered song search and matching platform for anyone in pursuit of songs, including the music industry, TV/Film and advertising.For Music and Creative Executives:MyPart’s award-winning technology uses reference songs- rather than conventional “tagging” techniques- to help A&Rs, Sync, and Music Supervisors “dig” throughand prioritize their musical catalogs and/or discover new talent. This helps these industry decision-makers find songs that share common harmonic, melodic, lyrical, sonic and structural features with their predefined benchmarks, and ensures they only listen to the “right” songs that actually match their needs. For Songwriters:MyPart’s AI analyzes songs submitted by emerging songwriters from all over the world, in order to place them with opportunities in the Music, TV/Film and Advertising Industries. We believe your songs deserve a shot, and create matches purely based on song relevance, rather than personal connections or industry access.MyPart graduated from Universal Music's exclusive Abbey Road RED program in London and Capitol Records' gBeta program in LA, as well as Deloitte's LaunchPad Growth program, and won the MassChallenge 2018 gold award and Music Week 2021 Tech Summit.Its proprietary 'Song Mining' engine conducts comprehensive and granular analysis of lyrical, musical and sonic relevance based on a wide technology stack that combines machine learning, computational linguistics, and digital signal processing to determine the likely relevance of any song to any set of reference songs. Our unique approach combines two key qualities:The ability to search for songs with a set of reference songs We created a song fingerprinting technique that abstracts song DNA to enable searching for songs with reference songs, based on common harmonic, melodic, lyrical, sonic and structural featuresA distinct focus on the semantics and aesthetics of song lyrics. At the heart of our distinct approach is a special focus on the deep comprehension of song lyrics