Artificial Intelligence for Lyrics Comprehension

Informacje na temat projektu

LyrAIcs

Identyfikator umowy o grant: 964009

DOI

10.3030/964009

Projekt został zamknięty

Data podpisania przez KE 17 Marca 2021

Data rozpoczęcia 1 Maja 2021

Data zakończenia 30 Kwietnia 2023

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

Brak danych

Wkład UE

€ 150 000,00

Koordynowany przez

IE UNIVERSIDAD
Spain

Periodic Reporting for period 1 - LyrAIcs (Artificial Intelligence for Lyrics Comprehension)

Okres sprawozdawczy: 2021-05-01 do 2023-04-30

Podsumowanie kontekstu i ogólnych celów projektu

The age of machine learning and data analytics have changed the habits of entertainment. Recommendation systems have been improving in the last years, with relevant commercial purposes, and many top-level companies –such as Amazon, Google or Netflix- are investing high amounts of money in improving their algorithms based on Artificial Intelligence. The case of music has been especially relevant, as the market has drastically changed in the last 10 years, moving towards a user-centric streaming model, where user preferences make the difference and dynamic playlists are the key of streaming success. Recommenders are built based on three main strategies:1) similarities between songs that are identified by their soundwaves; 2) classification using conventional tags for songs, such as author, genre, period or, in some cases, mood; and 3) collaborative tagging by users. In this context, song lyrics (the text of songs) are barely considered for the improvement of these strategies. Moreover, recommendations based on lyrics are done by hand with uneven criteria and filters. This Proof of Concept proposes the creation of an AI based recommendation engine (i.e. web service API) for analyzing song lyrics using POSTDATA ERC Project algorithms as its technical scaffold. Natural Language Processing tools for poetry analysis will be used to build a web service API to process lyrics and extract knowledge as additional metadata to enrich the companies’ recommender systems. This approach will open an exciting opportunity to contribute to boosting the music entertainment world using artificial intelligence and language technologies.