PBSCR: The Piano Bootleg Score Composer Recognition Dataset

• URL: http://arxiv.org/abs/2401.16803v3
• Date: Mon, 5 Aug 2024 21:55:11 GMT
• Title: PBSCR: The Piano Bootleg Score Composer Recognition Dataset
• Authors: Arhan Jain, Alec Bunn, Austin Pham, TJ Tsai,
• Abstract summary: PBSCR is a dataset for studying composer recognition of classical piano music. It contains 40,000 62x64 bootleg score images for a 9-class recognition task, 100,000 62x64 bootleg score images for a 100-class recognition task, and 29,310 unlabeled variable-length bootleg score images for pretraining.
• Score: 5.314803183185992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article motivates, describes, and presents the PBSCR dataset for studying composer recognition of classical piano music. Our goal was to design a dataset that facilitates large-scale research on composer recognition that is suitable for modern architectures and training practices. To achieve this goal, we utilize the abundance of sheet music images and rich metadata on IMSLP, use a previously proposed feature representation called a bootleg score to encode the location of noteheads relative to staff lines, and present the data in an extremely simple format (2D binary images) to encourage rapid exploration and iteration. The dataset itself contains 40,000 62x64 bootleg score images for a 9-class recognition task, 100,000 62x64 bootleg score images for a 100-class recognition task, and 29,310 unlabeled variable-length bootleg score images for pretraining. The labeled data is presented in a form that mirrors MNIST images, in order to make it extremely easy to visualize, manipulate, and train models in an efficient manner. We include relevant information to connect each bootleg score image with its underlying raw sheet music image, and we scrape, organize, and compile metadata from IMSLP on all piano works to facilitate multimodal research and allow for convenient linking to other datasets. We release baseline results in a supervised and low-shot setting for future works to compare against, and we discuss open research questions that the PBSCR data is especially well suited to facilitate research on.

Related papers

• Toward a More Complete OMR Solution [49.74172035862698]
  Optical music recognition aims to convert music notation into digital formats. One approach to tackle OMR is through a multi-stage pipeline, where the system first detects visual music notation elements in the image. We introduce a music object detector based on YOLOv8, which improves detection performance. Second, we introduce a supervised training pipeline that completes the notation assembly stage based on detection output.
  arXiv  Detail & Related papers  (2024-08-31T01:09:12Z)
• Self-Supervised Contrastive Learning for Robust Audio-Sheet Music Retrieval Systems [3.997809845676912]
  We show that self-supervised contrastive learning can mitigate the scarcity of annotated data from real music content. We employ the snippet embeddings in the higher-level task of cross-modal piece identification. In this work, we observe that the retrieval quality improves from 30% up to 100% when real music data is present.
  arXiv  Detail & Related papers  (2023-09-21T14:54:48Z)
• Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features [32.138956674478116]
  Given a query composed of a reference image and a relative caption, the Composed Image Retrieval goal is to retrieve images visually similar to the reference one. We use features from the OpenAI CLIP model to tackle the considered task. We train a Combiner network that learns to combine the image-text features integrating the bimodal information.
  arXiv  Detail & Related papers  (2023-08-22T15:03:16Z)
• MARBLE: Music Audio Representation Benchmark for Universal Evaluation [79.25065218663458]
  We introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines.
  arXiv  Detail & Related papers  (2023-06-18T12:56:46Z)
• PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining [68.84339672878066]
  We introduce PyramidCLIP, which constructs an input pyramid with different semantic levels, and aligns visual elements and linguistic elements in the form of hierarchy. Experiments on three downstream tasks, including zero-shot image classification, zero-shot image-text retrieval and image object detection, verify the effectiveness of the proposed PyramidCLIP.
  arXiv  Detail & Related papers  (2022-04-29T13:38:42Z)
• Learning Co-segmentation by Segment Swapping for Retrieval and Discovery [67.6609943904996]
  The goal of this work is to efficiently identify visually similar patterns from a pair of images. We generate synthetic training pairs by selecting object segments in an image and copy-pasting them into another image. We show our approach provides clear improvements for artwork details retrieval on the Brueghel dataset.
  arXiv  Detail & Related papers  (2021-10-29T16:51:16Z)
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision [57.031588264841]
  We leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme.
  arXiv  Detail & Related papers  (2021-02-11T10:08:12Z)
• Composer Style Classification of Piano Sheet Music Images Using Language Model Pretraining [16.23438816698455]
  We recast the problem to be based on raw sheet music images rather than a symbolic music format. Our approach first converts the sheet music image into a sequence of musical "words" based on the bootleg feature representation. We train AWD-LSTM, GPT-2, and RoBERTa language models on all piano sheet music images in IMSLP.
  arXiv  Detail & Related papers  (2020-07-29T04:13:59Z)
• Camera-Based Piano Sheet Music Identification [19.850248946069023]
  We use all solo piano sheet music images in the entire IMSLP dataset as a searchable database. We propose a novel hashing scheme called dynamic n-gram fingerprinting that significantly reduces runtime. In experiments on IMSLP data, our proposed method achieves a mean reciprocal rank of 0.85 and an average runtime of 0.98 seconds per query.
  arXiv  Detail & Related papers  (2020-07-29T03:55:27Z)
• Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-based Image Retrieval [55.29233996427243]
  Low-shot sketch-based image retrieval is an emerging task in computer vision. In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks. For solving these tasks, we propose a semantically aligned cycle-consistent generative adversarial network (SEM-PCYC) Our results demonstrate a significant boost in any-shot performance over the state-of-the-art on the extended version of the Sketchy, TU-Berlin and QuickDraw datasets.
  arXiv  Detail & Related papers  (2020-06-20T22:43:53Z)

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.