dMelodies: A Music Dataset for Disentanglement Learning
- URL: http://arxiv.org/abs/2007.15067v1
- Date: Wed, 29 Jul 2020 19:20:07 GMT
- Title: dMelodies: A Music Dataset for Disentanglement Learning
- Authors: Ashis Pati, Siddharth Gururani, Alexander Lerch
- Abstract summary: We present a new symbolic music dataset that will help researchers demonstrate the efficacy of their algorithms on diverse domains.
This will also provide a means for evaluating algorithms specifically designed for music.
The dataset is large enough (approx. 1.3 million data points) to train and test deep networks for disentanglement learning.
- Score: 70.90415511736089
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Representation learning focused on disentangling the underlying factors of
variation in given data has become an important area of research in machine
learning. However, most of the studies in this area have relied on datasets
from the computer vision domain and thus, have not been readily extended to
music. In this paper, we present a new symbolic music dataset that will help
researchers working on disentanglement problems demonstrate the efficacy of
their algorithms on diverse domains. This will also provide a means for
evaluating algorithms specifically designed for music. To this end, we create a
dataset comprising of 2-bar monophonic melodies where each melody is the result
of a unique combination of nine latent factors that span ordinal, categorical,
and binary types. The dataset is large enough (approx. 1.3 million data points)
to train and test deep networks for disentanglement learning. In addition, we
present benchmarking experiments using popular unsupervised disentanglement
algorithms on this dataset and compare the results with those obtained on an
image-based dataset.
Related papers
- Continual Learning for Multimodal Data Fusion of a Soft Gripper [1.0589208420411014]
A model trained on one data modality often fails when tested with a different modality.
We introduce a continual learning algorithm capable of incrementally learning different data modalities.
We evaluate the algorithm's effectiveness on a challenging custom multimodal dataset.
arXiv Detail & Related papers (2024-09-20T09:53:27Z) - The Brain's Bitter Lesson: Scaling Speech Decoding With Self-Supervised Learning [3.649801602551928]
We develop a set of neuroscience-inspired self-supervised objectives, together with a neural architecture, for representation learning from heterogeneous recordings.
Results show that representations learned with these objectives scale with data, generalise across subjects, datasets, and tasks, and surpass comparable self-supervised approaches.
arXiv Detail & Related papers (2024-06-06T17:59:09Z) - Learning from the Best: Active Learning for Wireless Communications [9.523381807291049]
Active learning algorithms identify the most critical and informative samples in an unlabeled dataset and label only those samples, instead of the complete set.
We present a case study of deep learning-based mmWave beam selection, where labeling is performed by a compute-intensive algorithm based on exhaustive search.
Our results show that using an active learning algorithm for class-imbalanced datasets can reduce labeling overhead by up to 50% for this dataset.
arXiv Detail & Related papers (2024-01-23T12:21:57Z) - A Weighted K-Center Algorithm for Data Subset Selection [70.49696246526199]
Subset selection is a fundamental problem that can play a key role in identifying smaller portions of the training data.
We develop a novel factor 3-approximation algorithm to compute subsets based on the weighted sum of both k-center and uncertainty sampling objective functions.
arXiv Detail & Related papers (2023-12-17T04:41:07Z) - Data Augmentations in Deep Weight Spaces [89.45272760013928]
We introduce a novel augmentation scheme based on the Mixup method.
We evaluate the performance of these techniques on existing benchmarks as well as new benchmarks we generate.
arXiv Detail & Related papers (2023-11-15T10:43:13Z) - Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music
Generation Task [86.72661027591394]
We generate complete and semantically consistent symbolic music scores from text descriptions.
We explore the efficacy of using publicly available checkpoints for natural language processing in the task of text-to-music generation.
Our experimental results show that the improvement from using pre-trained checkpoints is statistically significant in terms of BLEU score and edit distance similarity.
arXiv Detail & Related papers (2022-11-21T07:19:17Z) - Pitch-Informed Instrument Assignment Using a Deep Convolutional Network
with Multiple Kernel Shapes [22.14133334414372]
This paper proposes a deep convolutional neural network for performing note-level instrument assignment.
Experiments on the MusicNet dataset using 7 instrument classes show that our approach is able to achieve an average F-score of 0.904.
arXiv Detail & Related papers (2021-07-28T19:48:09Z) - Fast accuracy estimation of deep learning based multi-class musical
source separation [79.10962538141445]
We propose a method to evaluate the separability of instruments in any dataset without training and tuning a neural network.
Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches.
arXiv Detail & Related papers (2020-10-19T13:05:08Z) - Detecting Generic Music Features with Single Layer Feedforward Network
using Unsupervised Hebbian Computation [3.8707695363745223]
The authors extract information on such features from a popular open-source music corpus.
They apply unsupervised Hebbian learning techniques on their single-layer neural network using the same dataset.
The unsupervised training algorithm enhances their proposed neural network to achieve an accuracy of 90.36% for successful music feature detection.
arXiv Detail & Related papers (2020-08-31T13:57:31Z) - Learning from Noisy Similar and Dissimilar Data [84.76686918337134]
We show how to learn a classifier from noisy S and D labeled data.
We also show important connections between learning from such pairwise supervision data and learning from ordinary class-labeled data.
arXiv Detail & Related papers (2020-02-03T19:59:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.