It is found that CRNN show a strong performance with respect to the number of parameter and training time, indicating the effectiveness of its hybrid structure in music feature extraction and feature summarisation.

517

Content

Lyra Dataset

MELON

0

Paper Graph

Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw Waveforms

Juhan Nam, Jongpil Lee, Jiyoung Park, K. Kim•Sun Mar 05 2017

The experiments show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million Song Dataset.

199 0

Paper Graph

Transfer Learning for Music Classification and Regression Tasks

Kyunghyun Cho, Keunwoo Choi, György Fazekas, Mark B. Sandler•Sun Mar 26 2017

This paper proposes to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network, and shows how it can serve as general-purpose music representation.

246 0

Paper Graph

Toward Universal Text-To-Music Retrieval

Juhan Nam, Seungheon Doh, Minz Won, Keunwoo Choi•Fri Nov 25 2022

This work reviews recent text-based music retrieval systems using a proposed benchmark in two main aspects: input text representation and training objectives and enables a universal text-to-music retrieval system that achieves comparable retrieval performances in both tag- and sentence-level inputs.

41 0

Paper Graph

Word-Level Embeddings for Cross-Task Transfer Learning in Speech Processing

M. Kegler, P. Beckmann, M. Cernak•Mon Oct 21 2019

This work introduces an encoder capturing word-level representations of speech for cross-task transfer learning and shows that the speech representation captured by the encoder through the pre-training is transferable across distinct speech processing tasks and datasets.

5 0

Paper Graph

CLaMP: Contrastive Language-Music Pre-training for Cross-Modal Symbolic Music Information Retrieval

Shangda Wu, Maosong Sun, Xu Tan, Dingyao Yu•Thu Apr 20 2023

In comparison to state-of-the-art models that require fine-tuning, zero-shot CLaMP demonstrated comparable or superior performance on score-oriented datasets, surpassing the capabilities of previous models.

18 0

Paper Graph

Pre-training Music Classification Models via Music Source Separation

P. Maragos, Athanasia Zlatintsi, Christos Garoufis•Mon Oct 23 2023

Experimental results indicate that pre-training the U-Nets with a music source separation objective can improve performance compared to both training the whole network from scratch and using the tail network as a standalone in two music classification tasks, music auto-tagging and music genre classification.

2 0

Paper Graph

Explaining Deep Convolutional Neural Networks on Music Classification

Keunwoo Choi, György Fazekas, Mark B. Sandler•Thu Jul 07 2016

It is shown that in the deep layers of a 5-layer CNN, the features are learnt to capture textures, the patterns of continuous distributions, rather than shapes of lines.

50 0

Paper Graph

Zero-shot Learning for Audio-based Music Classification and Tagging

Juhan Nam, Jeong Choi, Jongpil Lee, Jiyoung Park•Thu Jul 04 2019

This work investigates the zero-shot learning in the music domain and organizes two different setups of side information using human-labeled attribute information based on Free Music Archive and OpenMIC-2018 datasets and general word semantic information from Million Song Dataset and this http URL tag annotations.

49 0

Paper Graph

Multi-Level and Multi-Scale Feature Aggregation Using Sample-level Deep Convolutional Neural Networks for Music Classification

Juhan Nam, Jongpil Lee•Tue Jun 20 2017

This work proposes a music classification approach that aggregates multi-level and multi-scale features using pre-trained feature extractors trained in sample-level deep convolutional neural networks using raw waveforms.

14 0

Paper Graph

Adding a benchmark result helps the community track progress.