MyPart Inc.
Amir Graitzer is an experienced professional with a diverse background in technology and music. Amir is currently serving as the Chief Technology Officer at MyPart Inc. since June 2022, where they are responsible for leading the technology department. Prior to this, they held the role of Research Lead at MyPart Inc. from October 2020 to June 2022, where they contributed to research initiatives. Before joining MyPart Inc., Amir worked as a Machine Learning & Full Stack Engineer at the same company from September 2019 to October 2020.
Amir's passion for music led him to found the Music, Technology & Innovation Program at Rimon School of Music in 2018. This innovative program integrated music theory and practice with entrepreneurship and programming courses. Amir served as the Founder of the Music, Technology & Innovation Program until June 2020 and also worked as a Lecturer at Rimon School of Music from October 2016 to June 2020.
In addition to their work in the technology and education sectors, Amir has experience as a Full Stack Developer at Tonara from April 2015 to July 2018. Amir also worked as a Machine Learning Intern at Bar-Ilan University from September 2014 to September 2015.
Amir's early career included a role as a Full Stack Developer at MediaMind from March 2012 to March 2014, where they were responsible for processing data and developing various applications. Prior to that, they worked as a Music Teacher at Yamaha from August 2008 to March 2012, where they taught guitar and music theory to students of all ages and levels. Amir also worked as a Music Teacher at Musical from March 2008 to March 2010.
Overall, Amir Graitzer has a strong background in technology, research, and music education, with a focus on innovation and integrating technology into traditional practices.
Amir Graitzer has an extensive education history spanning various fields. In 2014, they pursued a Master of Science (MSc) degree in Computational Neuroscience at Tel Aviv University, which they completed in 2016. Prior to that, from 2009 to 2013, they obtained a Bachelor of Science (B.Sc) degree in Computer Science from the same university. Furthermore, between 2007 and 2011, Graitzer pursued a Bachelor of Arts (BA) degree in Philosophy, also at Tel Aviv University. Additionally, they attended the Rimon School of Jazz and Contemporary Music from 2000 to 2002, where they focused on Music Theory and Guitar.
In terms of certifications, Graitzer earned a "R Programming" certification from Coursera Verified Certificates in September 2014. Amir also obtained a certification for "The Data Scientist's Toolbox" in September 2014, also from Coursera Verified Certificates.
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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