Registration for ICASSP is free of charge, but registration is required to view the videos. If you have not yet registered, please visit: https://cmsworkshops.com/ICASSP2020/Registration.asp.Access the full virtual conference by visiting: https://2020.ieeeicassp-virtual.org/attendee/login. Your username is your email address and your password is your confirmation number/registration ID.

You need an account to view media

Sign in to view media

Don't have an account? Please contact us to request an account.

Audio and Acoustic Signal Processing
AUD-L1.5
Lecture
Music Information Retrieval I

ACCURATE AND SCALABLE VERSION IDENTIFICATION USING MUSICALLY-MOTIVATED EMBEDDINGS

Furkan Yesiler

Date & Time

Tue, May 5, 2020

12:30 pm – 2:30 pm

Location

On-Demand

Abstract

The version identification (VI) task deals with the automatic detection of recordings that correspond to the same underlying musical piece. Despite many efforts, VI is still an open problem, with much room for improvement, specially with regard to combining accuracy and scalability. In this paper, we present MOVE, a musically-motivated method for accurate and scalable version identification. MOVE achieves state-of-the-art performance on two publicly-available benchmark sets by learning scalable embeddings in an Euclidean distance space, using a triplet loss and a hard triplet mining strategy. It improves over previous work by employing an alternative input representation, and introducing a novel technique for temporal content summarization, a standardized latent space, and a data augmentation strategy specifically designed for VI. In addition to the main results, we perform an ablation study to highlight the importance of our design choices, and study the relation between embedding dimensionality and model performance.


Description


Presenter

Furkan Yesiler

Universitat Pompeu Fabra
Sign in to join the conversationDon't have an account? Please contact us to request an account.
Sign in to view documentsDon't have an account? Please contact us to request an account.

Session Chair

Emmanouil Benetos

Queen Mary University of London