Retrieving Music Semantics from Optical Music Recognition by Machine Translation

In this paper, we apply machine translation techniques to solve one of the central problems in the field of optical music recognition: extracting the semantics of a sequence of music characters. So far, this problem has been approached through heuristics and grammars, which are not generalizable solutions. We borrowed the seq2seq model and the attention mechanism from machine translation to address this issue. Given its example-based learning, the model proposed is meant to apply to different notations provided there is enough training data. The model was tested on the PrIMuS dataset of common Western music notation incipits. Its performance was satisfactory for the vast majority of examples, flawlessly extracting the musical meaning of 85% of the incipits in the test set—mapping correctly series of accidentals into key signatures, pairs of digits into time signatures, combinations of digits and rests into multi-measure rests, detecting implicit accidentals, etc.This work is supported by the Spanish Ministry HISPAMUS project TIN2017-86576-R, partially funded by the EU, and by CIRMMT’s Inter-Centre Research Exchange Funding and McGill’s Graduate Mobility Award

Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords

End-to-End Neural Optical Music Recognition of Monophonic Scores

[EN] Optical Music Recognition is a field of research that investigates how to computationally decode music notation from images. Despite the efforts made so far, there are hardly any complete solutions to the problem. In this work, we study the use of neural networks that work in an end-to-end manner. This is achieved by using a neural model that combines the capabilities of convolutional neural networks, which work on the input image, and recurrent neural networks, which deal with the sequential nature of the problem. Thanks to the use of the the so-called Connectionist Temporal Classification loss function, these models can be directly trained from input images accompanied by their corresponding transcripts into music symbol sequences. We also present the Printed Images of Music Staves (PrIMuS) dataset, containing more than 80,000 monodic single-staff real scores in common western notation, that is used to train and evaluate the neural approach. In our experiments, it is demonstrated that this formulation can be carried out successfully. Additionally, we study several considerations about the codification of the output musical sequences, the convergence and scalability of the neural models, as well as the ability of this approach to locate symbols in the input score.This work was supported by the Social Sciences and Humanities Research Council of Canada, and the Spanish Ministerio de Economia y Competitividad through Project HISPAMUS Ref. No. TIN2017-86576-R (supported by UE FEDER funds).Calvo-Zaragoza, J.; Rizo, D. (2018). End-to-End Neural Optical Music Recognition of Monophonic Scores. Applied Sciences. 8(4). https://doi.org/10.3390/app8040606S8

Retrieving Music Semantics from Optical Music Recognition by Machine Translation

In this paper, we apply machine translation techniques to solve one of the central problems in the field of optical music recognition: extracting the semantics of a sequence of music characters. So far, this problem has been approached through heuristics and grammars, which are not generalizable solutions. We borrowed the seq2seq model and the attention mechanism from machine translation to address this issue. Given its example-based learning, the model proposed is meant to apply to different notations provided there is enough training data. The model was tested on the PrIMuS dataset of common Western music notation incipits. Its performance was satisfactory for the vast majority of examples, flawlessly extracting the musical meaning of 85% of the incipits in the test set—mapping correctly series of accidentals into key signatures, pairs of digits into time signatures, combinations of digits and rests into multi-measure rests, detecting implicit accidentals, etc

Optical Music Recognition

Nowadays records, radio, television and the internet spread music more widely than ever before, and an overwhelming number of musical works are available to us. During the last decades, a great interest in converting music scores into a computer-readable format has arisen, and with this the field of Optical Music Recognition. Optical Music Recognition (OMR) is the name of systems for music score recognition, and is similar to Optical Character Recognition (OCR) except that it is used to recognize musical symbols instead of letters. OMR systems try to automatically recognize the main musical objects of a scanned music score and convert them into a suitable electronic format, such as a MIDI file, an audio waveform or ABC Notation. The advantage of such a digital format, compared to retaining the whole image of a music score, is that only the semantics of music are stored, that is notes, pitches and durations, contextual information and other relevant information. This way much computer space is saved, and at the same time scores can be printed over and over again, without loss of quality, and they can be edited and played on a computer \citep{Vieira01}. OMR may also be used for educational reasons - to convert scores into Braille code for blind people, to generate customized version of music exercises etc. In addition, this technology can be used to index and collect scores in databases. Today, there are a number of on-line databases containing digital sheet music, making music easily available for everyone, free of charge. The earliest attempts at OMR were made in the early 1970's. During the last decades, OMR has been especially active, and there are currently a number of commercially available packages. The first commercial products came in the early 90's. However, in most cases these systems operate properly only with well-scanned documents of high quality. When it comes to precision and reliability, none of the commercial OMR systems solve the problem in a satisfactory way. The aim of this thesis is to study various existing OMR approaches and suggest novel methods, or modifications/improvements of current algorithms. The first stages of the process is prioritized, and we limit to concentrate on identifying the main musical symbols, essential for playing the melody, while text, slurs, staff numbering etc. are ignored by our program. The last part of an OMR program usually consists of correcting classification errors by introducing musical rules. In this thesis, this is only applied to correct wrongly classified pitched for accidentals

Applying Automatic Translation for Optical Music Recognition’s Encoding Step

Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results.This work was supported by the Spanish Ministry HISPAMUS project TIN2017-86576-R, partially funded by the EU, and by the Generalitat Valenciana through project GV/2020/030

Staff-line detection and removal using a convolutional neural network

Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939), the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds) and the Instituto Universitario de Investigación Informática (IUII) from the University of Alicante

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience