pith. sign in

arxiv: 1807.09208 · v1 · pith:MNQWRNKNnew · submitted 2018-07-24 · 💻 cs.SD · eess.AS

A Hybrid of Deep Audio Feature and i-vector for Artist Recognition

classification 💻 cs.SD eess.AS
keywords artistapproachdeephybridi-vectormodelrecognitionachieves
0
0 comments X
read the original abstract

Artist recognition is a task of modeling the artist's musical style. This problem is challenging because there is no clear standard. We propose a hybrid method of the generative model i-vector and the discriminative model deep convolutional neural network. We show that this approach achieves state-of-the-art performance by complementing each other. In addition, we briefly explain the advantages and disadvantages of each approach.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Acoustic Modeling for Automatic Lyrics-to-Audio Alignment

    eess.AS 2019-06 unverdicted novelty 4.0

    Adding voicing and auditory features and adapting solo-singing models with limited polyphonic data reduces alignment errors for lyrics in polyphonic audio.