Search Results (4)

Manual transcription of music is a tedious work, which can be greatly facilitated by optical music recognition (OMR) software. However, OMR software is error prone in particular for older handwritten documents. This paper introduces and evaluates a pipeline that automates the entire OMR workflow in the context of the Corpus Monodicum project, enabling the transcription of historical chants. In addition to typical OMR tasks such as staff line detection, layout detection, and symbol recognition, the rarely addressed tasks of text and syllable recognition and assignment of syllables to symbols are tackled. For quantitative and qualitative evaluation, we use documents written in square notation developed in the 11th–12th century, but the methods apply to many other notations as well. Quantitative evaluation measures the number of necessary interventions for correction, which are about 0.4% for layout recognition including the division of text in chants, 2.4% for symbol recognition including pitch and reading order and 2.3% for syllable alignment with correct text and symbols. Qualitative evaluation showed an efficiency gain compared to manual transcription with an elaborate tool by a factor of about 9. In a second use case with printed chants in similar notation from the “Graduale Synopticum”, the evaluation results for symbols are much better except for syllable alignment indicating the difficulty of this task. Full article

Optical music recognition (OMR) refers to converting musical scores into digitized information using electronics. In recent years, few types of OMR research have involved numbered musical notation (NMN). The existing NMN recognition algorithm is difficult to deal with because the numbered notation font is changing. In this paper, we made a multi-font NMN dataset. Using the presented dataset, we use kernel density estimation with proposed bar line criteria to measure the relative height of symbols, and an accurate separation of melody lines and lyrics lines in musical notation is achieved. Furthermore, we develop a structurally improved convolutional neural network (CNN) to classify the symbols in melody lines. The proposed neural network performs hierarchical processing of melody lines according to the symbol arrangement rules of NMN and contains three parallel small CNNs called Arcnet, Notenet and Linenet. Each of them adds a spatial pyramid pooling layer to adapt to the diversity of symbol sizes and styles. The experimental results show that our algorithm can accurately detect melody lines. Taking the average accuracy rate of identifying various symbols as the recognition rate, the improved neural networks reach a recognition rate of 95.5%, which is 8.5% higher than the traditional convolutional neural networks. Through audio comparison and evaluation experiments, we find that the generated audio maintains a high similarity to the original audio of the NMN. Full article

The algorithm presented in this paper provides the means for the real-time recognition of the key signature associated with a given piece of music, based on the analysis of a very small number of initial notes. The algorithm can easily be implemented in electronic musical instruments, enabling real-time generation of musical notation. The essence of the solution proposed herein boils down to the analysis of a music signature, defined as a set of twelve vectors representing the particular pitch classes. These vectors are anchored in the center of the circle of fifths, pointing radially towards each of the twelve tones of the chromatic scale. Besides a thorough description of the algorithm, the authors also present a theoretical introduction to the subject matter. The results of the experiments performed on preludes and fugues by J.S. Bach, as well as the preludes, nocturnes, and etudes of F. Chopin, validating the usability of the method, are also presented and thoroughly discussed. Additionally, the paper includes a comparison of the efficacies obtained using the developed solution with the efficacies observed in the case of music notation generated by a musical instrument of a reputable brand, which clearly indicates the superiority of the proposed algorithm. Full article

The ability to sight-read traditional staff notation is an important skill for all classically trained musicians. Up until now, however, most research has focused on pianists, by comparing experts and novices. Eye movement studies are a niche area of sight-reading research, focusing on eye-hand span and perceptual span of musicians, mostly pianists. Research into eye movement of non-piano sight-reading is limited. Studies into eye movement of woodwind sight-reading were conducted in the 1980s and early 2000s, highlighting the need for new research using modern equipment. This pilot study examined the eye movements of six woodwind (flute, clarinet) undergraduates of intermediate-to-advanced skill level during sight-reading of scores of increased difficulty. The data was analysed in relation to expertise level and task difficulty, focusing on numbers of fixations and fixation durations. The results show that as music examples became more difficult the numbers of fixations increased and fixation durations decreased; more experienced players with better sight-reading skills required less time to process musical notation; and participants with better sight-reading skills utilised fewer fixations to acquire information visually. The findings confirm that the efficiency of eye movements is related to instrumental and sight-reading expertise, and that task difficulty affects eye movement strategies. Full article