Data representations for audio-to-score monophonic music transcription

Román, Miguel A.; Pertusa, Antonio; Calvo-Zaragoza, Jorge

doi:10.1016/j.eswa.2020.113769

Cited by 20 publications

(8 citation statements)

References 11 publications

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“…In 2020, Miguel et al [2] developed an e2e technique depending on DNN for audio-to-score transcription of music from monophonic quotes. Here, an audio file was given as input that was modeled as a frame sequence, and a DNN was trained to provide a sequence of encoded music notes.…”

Section: Jazzmentioning

confidence: 99%

“…transforming an acoustic signal into a symbolic representation, which comprises notes, their pitches, timings, and a classification of the instruments used. AMT [1] [2] is the process of automatically converting a musical sound signal into its representation as musical notation, through digital analysis of the musical signal". The AMT was the objective of many researchers from the time of its establishment, and currently, it has covered a wider range of subtasks [3] [4].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Sagar Latake

2024

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In music, transcription is the practice of notating a piece or a sound which was previously unnotated and/or unpopular as a written music". An absolute transcription will be performed only when the timing, pitching, and instruments of all sound events are solved. In music transcription systems, a MIDI file is found to be a suitable format for melodic notations. This survey intends to make a review of 65 papers that concern music transcription using machine learning techniques. Accordingly, systematic analyses of the adopted techniques are carried out and presented briefly. The performances and related maximum achievements of each contribution are also portrayed in this survey. Moreover, the various datasets used in music transcription techniques were considered and reviewed in this work. Finally, the survey portrays the research problems and weaknesses that may be supportive for researchers to introduce the latest techniques related to music transcription.

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Section: Jazzmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Sagar Latake

2024

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“…In this paper, we introduce the Quartets dataset. Quartets is a well-known collection employed in the Audio to Score field [30,3]. As the dataset provides the Humdrum **kern transcriptions from the excerpts of music, we produced a single-system transcription version of it.…”

Section: Corporamentioning

confidence: 99%

On the Use of Transformers for End-to-End Optical Music Recognition

Ríos-Vila

Iñesta

Calvo-Zaragoza

2022

Lecture Notes in Computer Science

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State-of-the-art end-to-end Optical Music Recognition (OMR) has, to date, primarily been carried out using monophonic transcription techniques to handle complex score layouts, such as polyphony, often by resorting to simplifications or specific adaptations. Despite their efficacy, these approaches imply challenges related to scalability and limitations. This paper presents the Sheet Music Transformer (SMT), the first end-to-end OMR model designed to transcribe complex musical scores without relying solely on monophonic strategies. Our model employs a Transformer-based image-to-sequence framework that predicts score transcriptions in a standard digital music encoding format from input images. Our model has been tested on two polyphonic music datasets and has proven capable of handling these intricate music structures effectively. The experimental outcomes not only indicate the competence of the model, but also show that it is better than the state-of-the-art methods, thus contributing to advancements in end-to-end OMR transcription.

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“…Concerning the recognition architectures, we consider a Convolutional Recurrent Neural Network (CRNN) scheme to approximate g (•). Recent works have applied this approach to both OMR [5,6] and AMT [18,19] transcription systems with remarkably successful results. Hence, we shall resort to these works to define our baseline single-modality transcription architectures within the multimodal framework.…”

Section: Neural End-to-end Base Recognition Systemsmentioning

confidence: 99%

Multimodal image and audio music transcription

Fuente

Valero-Mas

Castellanos

et al. 2021

Int J Multimed Info Retr

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Optical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that aim at obtaining a structured digital representation from sheet music images and acoustic recordings, respectively. While these fields have traditionally evolved independently, the fact that both tasks may share the same output representation poses the question of whether they could be combined in a synergistic manner to exploit the individual transcription advantages depicted by each modality. To evaluate this hypothesis, this paper presents a multimodal framework that combines the predictions from two neural end-to-end OMR and AMT systems by considering a local alignment approach. We assess several experimental scenarios with monophonic music pieces to evaluate our approach under different conditions of the individual transcription systems. In general, the multimodal framework clearly outperforms the single recognition modalities, attaining a relative improvement close to $$40\%$$ 40 % in the best case. Our initial premise is, therefore, validated, thus opening avenues for further research in multimodal OMR-AMT transcription.

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Data representations for audio-to-score monophonic music transcription

Cited by 20 publications

References 11 publications

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

On the Use of Transformers for End-to-End Optical Music Recognition

Multimodal image and audio music transcription

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