Optical Music Recognition in Manuscripts from the Ricordi Archive

• URL: http://arxiv.org/abs/2408.10260v1
• Date: Wed, 14 Aug 2024 09:29:11 GMT
• Title: Optical Music Recognition in Manuscripts from the Ricordi Archive
• Authors: Federico Simonetta, Rishav Mondal, Luca Andrea Ludovico, Stavros Ntalampiras,
• Abstract summary: Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. We have automatically extracted samples that represent various musical elements depicted on the manuscripts, including notes, staves, clefs, erasures, and composer's annotations. We trained multiple neural network-based classifiers to differentiate between the identified music elements.
• Score: 6.274767633959002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Ricordi archive, a prestigious collection of significant musical manuscripts from renowned opera composers such as Donizetti, Verdi and Puccini, has been digitized. This process has allowed us to automatically extract samples that represent various musical elements depicted on the manuscripts, including notes, staves, clefs, erasures, and composer's annotations, among others. To distinguish between digitization noise and actual music elements, a subset of these images was meticulously grouped and labeled by multiple individuals into several classes. After assessing the consistency of the annotations, we trained multiple neural network-based classifiers to differentiate between the identified music elements. The primary objective of this study was to evaluate the reliability of these classifiers, with the ultimate goal of using them for the automatic categorization of the remaining unannotated data set. The dataset, complemented by manual annotations, models, and source code used in these experiments are publicly accessible for replication purposes.

Related papers

• Aligned Music Notation and Lyrics Transcription [8.411893399638046]
  This paper introduces and formalizes the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge. It addresses the complete transcription of vocal scores by jointly considering music symbols, lyrics, and their synchronization. We evaluate different approaches to address this challenge, ranging from traditional divide-and-conquer methods that handle music and lyrics separately, to novel end-to-end solutions.
  arXiv  Detail & Related papers  (2024-12-05T14:50:11Z)
• Automatic Estimation of Singing Voice Musical Dynamics [9.343063100314687]
  We propose a methodology for dataset curation. We compile a dataset comprising 509 musical dynamics annotated singing voice performances, aligned with 163 score files. We train a CNN model with varying window sizes to evaluate the effectiveness of estimating musical dynamics. We conclude through our experiments that bark-scale based features outperform log-Mel-features for the task of singing voice dynamics prediction.
  arXiv  Detail & Related papers  (2024-10-27T18:15:18Z)
• 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)
• GETMusic: Generating Any Music Tracks with a Unified Representation and Diffusion Framework [58.64512825534638]
  Symbolic music generation aims to create musical notes, which can help users compose music. We introduce a framework known as GETMusic, with GET'' standing for GEnerate music Tracks'' GETScore represents musical notes as tokens and organizes tokens in a 2D structure, with tracks stacked vertically and progressing horizontally over time. Our proposed representation, coupled with the non-autoregressive generative model, empowers GETMusic to generate music with any arbitrary source-target track combinations.
  arXiv  Detail & Related papers  (2023-05-18T09:53:23Z)
• RMSSinger: Realistic-Music-Score based Singing Voice Synthesis [56.51475521778443]
  RMS-SVS aims to generate high-quality singing voices given realistic music scores with different note types. We propose RMSSinger, the first RMS-SVS method, which takes realistic music scores as input. In RMSSinger, we introduce word-level modeling to avoid the time-consuming phoneme duration annotation and the complicated phoneme-level mel-note alignment.
  arXiv  Detail & Related papers  (2023-05-18T03:57:51Z)
• Melody transcription via generative pre-training [86.08508957229348]
  Key challenge in melody transcription is building methods which can handle broad audio containing any number of instrument ensembles and musical styles. To confront this challenge, we leverage representations from Jukebox (Dhariwal et al. 2020), a generative model of broad music audio. We derive a new dataset containing $50$ hours of melody transcriptions from crowdsourced annotations of broad music.
  arXiv  Detail & Related papers  (2022-12-04T18:09:23Z)
• A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
  This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
  arXiv  Detail & Related papers  (2022-11-21T14:15:43Z)
• Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings [36.090928638883454]
  Music-to-text synaesthesia aims to generate descriptive texts from music recordings with the same sentiment for further understanding. We build a computational model to generate sentences that can describe the content of the music recording. To tackle the highly non-discriminative classical music, we design a group topology-preservation loss.
  arXiv  Detail & Related papers  (2022-10-02T06:06:55Z)
• Lyric document embeddings for music tagging [0.38233569758620045]
  We present an empirical study on embedding the lyrics of a song into a fixed-dimensional feature for the purpose of music tagging. Five methods of computing token-level and four methods of computing document-level representations are trained on an industrial-scale dataset of tens of millions of songs. We find that averaging word embeddings outperform more complex architectures in many downstream metrics.
  arXiv  Detail & Related papers  (2021-11-29T11:02:24Z)
• Sequence Generation using Deep Recurrent Networks and Embeddings: A study case in music [69.2737664640826]
  This paper evaluates different types of memory mechanisms (memory cells) and analyses their performance in the field of music composition. A set of quantitative metrics is presented to evaluate the performance of the proposed architecture automatically.
  arXiv  Detail & Related papers  (2020-12-02T14:19:19Z)
• Vector-Quantized Timbre Representation [53.828476137089325]
  This paper targets a more flexible synthesis of an individual timbre by learning an approximate decomposition of its spectral properties with a set of generative features. We introduce an auto-encoder with a discrete latent space that is disentangled from loudness in order to learn a quantized representation of a given timbre distribution. We detail results for translating audio between orchestral instruments and singing voice, as well as transfers from vocal imitations to instruments.
  arXiv  Detail & Related papers  (2020-07-13T12:35:45Z)

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.