Piano Research Articles

Construction and Application of a Piano Playing Pitch Recognition Model Based on Neural Network.

The intonation recognition of piano scores is an important problem in the field of music information retrieval. Based on the neural network theory, this study constructs a piano playing intonation recognition model and uses the optimized result as the feature of piano music to realize the prediction of the music recognition of the intonation preference. The model combines the behavioral preference relationship between intonation and musical notation to measure the similarity between intonations, which is used to calculate the similarity between intonation preference and music, and solves the quantification problem of intonation recognition. In the simulation process, the pitch preference feature of piano playing is used as the identification basis, and the effectiveness of the algorithm is verified through four sets of experiments. The experimental results show that the average symbol error rate of the improved network model is reduced to 0.3234%, and the model training time is about 33.3% of the traditional convolutional recurrent neural network, which is optimized in terms of recognition accuracy and training time in single-class pitch feature. In the recommended method of multi-category evaluation of pitch features, the recognition accuracy of multi-category pitch features is 42.89%, which effectively improves the musical tone recognition rate.

The Efficacy of Binaural Beat Stimulation Mixed with Acoustic Music in Chronic Low Back Pain Management: A Randomized Controlled Trial

Background: By using an external stimulus with different beat frequencies to generate an optimal brain wave pattern, binaural beat stimulation provides a similar effect to mindfulness meditation. While it has been found to have a beneficial effect in chronic pain conditions, its effect in patients suffering from low back pain has not been examined. Objective: To investigate the efficacy of binaural beat stimulation mixed with acoustic music compared to acoustic music alone in individuals with chronic low back pain. Materials and Methods: Adults with chronic low back pain were randomly allocated into two groups, where Group A listened to 20 minutes of acoustic piano music per day for 14 days, and patients in group B listened to 20 minutes of piano music mixed with 6 Hz-theta binaural beats per day for 14 days. The primary outcome was the pain score at 14 days as measured by the Thai Brief Pain Inventory (BPI). The secondary outcomes included quantitative electroencephalogram (QEEG) changes, pain interference, and changes in the Thai Hospital Anxiety Depression Scale (HADS). All participants, outcome assessors and QEEG evaluator were blinded. Results: Twelves participants were enrolled in each group. No intergroup differences were found in pain, pain interference, or QEEG measures. In addition, a significant improvement in the HADS-D (depression subscale) was found in both groups. Conclusion: The authors were unable to find a benefit to the addition of binaural beats stimulation to acoustic music in patients suffering from chronic low back pain. More studies are warranted. Keywords: Binaural beat stimulation; Chronic low back pain; Theta brain wave

Research on the development of piano performance teaching in China

The establishment of Shanghai National Conservatory of music in 1927 marked the establishment of piano performance in China. With the development of the motherland, Chinese piano art has grown up all the way, showing unique musical cultural phenomena and Piano cultural highlights closely related to the social environment in different historical stages. The author believes that the development of Chinese piano art is mainly reflected in three aspects: teaching, creation and performance. The pioneers of China's early piano education not only trained the first batch of piano players for the Chinese music world, but also continued their teaching ideas, teaching styles and teaching methods on their students from generation to generation, which has had a positive and far-reaching impact on today's Piano Teaching in China. By sorting out and analyzing the historical development stages of Chinese piano performance teaching theory, the author can observe the current situation of Chinese piano performance teaching with a more critical perspective, thus enlightening us to explore and think about Chinese piano performance teaching in the new era, so as to promote the deeper and broader development of the research on piano performance teaching theory, And try to change the problem that piano performance teaching only stays at the level of language description and experience summary to a certain extent, and seek a development path for the improvement of piano teachers' scientific research ability.

Listening to Messiaen’s colourful hands

Name: Arjen Berends Main subject: Master Theory of Music Supervisor: Dr. Bert Mooiman Title of Research: Listening to Messiaen's colourful hands Subtitle: Analysing harmony and voice leading in the two homorhythmic chorales from Messiaen’s La Transfiguration de Notre Seigneur Jésus-Christ. Research question: How to analyse harmony and voice leading in the two homorhythmic chorales from Messiaen’s La Transfiguration de Notre Seigneur Jésus-Christ? Abstract Olivier Messiaen (1908-1992), one of the most famous French composers of the 20th century, was also known to be a great organist and improviser, as organist of Église de la Sainte-Trinité in Paris for over 60 years. His huge oratorio La Transfiguration de Notre Seigneur Jésus-Christ, composed 1965-1969, includes two entirely homorhythmic movements entitled ‘Choral’. As a listener, I am fascinated by Messiaen's extraordinary approach to harmony. The aim of this research is, therefore, to attempt to understand how best to analyse Messiaen's unique harmonic language. Many approaches to analysing Messiaen’s music are vertically orientated, including his own way of describing his use of harmony. He himself never discussed the horizontal aspects of his music. Because of his coloured hearing synaesthesia (son-couleur), Messiaen's analyses are particularly special in their manner of addressing the colours of certain chords. A colour analysis by Messiaen therefore may give the listener some insight in the way Messiaen might have perceived his music. This research includes videos with the music and paintings of the colour analyses by Messiaen and two other musicians with synaesthesia of one of the chorales, specially made for this research. Considering Messiaen’s own lack of emphasis on horizontal aspects of his music, is it possible to understand Messiaen’s music from a horizontal perspective as well? Since the two chorales are homophonic and homorhythmic, the bass and soprano are the most important voices. These parts can be perceived clearly, but the attention of the listener is also caught by other aspects such as the complexity of the chords. It is difficult to determine the precise voice leading of the inner parts because it is ‘blurred’ by the varied orchestration, but by way of harmonic reductions of the chorales, Messiaen’s horizontal compositional techniques can be clarified. This research includes a harmonisation of a melody by Messiaen in the style of the chorales in La Transfiguration. Biography Arjen Berends teaches music theory and arranging at the conservatories of The Hague and Utrecht. He graduated in music education at the Utrecht Conservatory, where he also studied classical piano with Henk Ekkel and Martyn van den Hoek. At the Royal Conservatoire of The Hague he completed his studies in music theory with Paul Scheepers, Arie Boers, Diderik Wagenaar, and Ineke Kien. He is répétiteur of Toonkunstkoor Amsterdam conducted by Boudewijn Jansen; he has previously worked with choral conductors such as Louis Buskens, Iassen Raykov and Béni Csillag.

Optimized Piano Music Education Model Based on Multimodal Information Fusion for Emotion Recognition in Multimedia Video Networks

Emotion is the important information that people transmit in the process of communication, and the change of emotional state affects people’s perception and decision-making, which introduces the emotional dimension into human-computer interaction. The modes of emotional expression include facial expressions, speech, posture, physiological signals, text, and so on. Emotion recognition is essentially a multimodal fusion problem. This paper investigates the different teaching modes of the teachers and students of our school, designs the load capacity through the K-means algorithm, builds a multimedia network sharing classroom, and creates a piano music situation to stimulate students’ learning interest, using audiovisual and other tools to mobilize students’ emotions, using multimedia guidance to extend students’ piano music knowledge, and comprehensively improve students’ aesthetic ability and autonomous learning ability. Comparing the changes of students after 3 months of teaching, the results of the study found that multimedia sharing classrooms can be up to 50% ahead of traditional teaching methods in enhancing students’ interest, and teachers’ acceptance of multimedia network sharing classrooms is also high.

Improvement of Speech Recognition Technology in Piano Music Scene Based on Deep Learning of Internet of Things.

The main goal of speech recognition technology is to use computers to convert human analog speech signals into computer-generated signals, such as behavior patterns or binary codes. Different from speaker identification and speaker confirmation, the latter attempts to identify or confirm the speaker who uttered the speech rather than the lexical content contained in it. The short-term idea is that it should be able to record the musical sound played by the user with a certain musical instrument, then extract the note and duration information from it, and finally generate the corresponding MID file according to the MIDI standard, which can set the type of musical instrument in advance to complete the function of musical sound transformation, such as playing with a harmonica, and playing the MID at the end is the piano sound. With the rapid development of the mobile Internet, fields such as machine learning, electronic communication, and navigation have placed high demands on real-time and standard text recognition technology. This paper merges the sound of visual music into text-based data set training, uses the exported scanner features for model training, uses the model to extract features, then uses the features for prior training, and then uses pretraining. DNN results show that the combined training of target prevention and expansion plans, by replacing long-term and short-term memory networks, end-to-end speech recognition programs, and behavioral tests organized by mobile devices, can provide a larger receptive field combined with expanded convolution instead of long and short periods. The experimental results show that when the input sampling point is 2400, it can be seen that the convergence speed of the model becomes slower with more than 90 iterations and the loss of the model on the verification set increases with the increase in the number of iterations. This shows that the model in this paper can fully meet the needs of speech recognition in piano music scenes.

Design and Application of the Piano Teaching System Integrating Videos and Images

The art of piano playing has been continuously entering into people’s life. However, with the continuous improvement of science and technology and living standards, the traditional teaching mode can no longer meet the piano teaching mode. The teaching of piano is different from traditional subjects, such as Chinese and mathematics. It requires students to experience the artistic characteristics and the live atmosphere of the players brought by the piano. This study integrates video and image teaching methods with piano teaching. Videos and images can more intuitively show the live atmosphere brought by piano players and musical artistic features brought by the piano. At the same time, this study uses the convolutional neural network (CNN) method to study the relevant features of videos and images of piano teaching. These features are mainly the characteristics of piano music, the behavior of players, and the basic knowledge of a piano. The research results show that the clustering method can effectively classify the features of videos and images in piano teaching, and the maximum classification error is only 1.89%. The CNN method also has high performance in predicting the relevant features of piano teaching videos and images. Accuracy. The largest prediction error is only 2.23%, and the linear correlation coefficient also exceeds 0.95. This set of the piano teaching mode that combines videos and images is beneficial to both teachers and students.

Effects of piano music of different tempos on heart rate and autonomic nervous system during the recovery period after high-intensity exercise.

PurposeThe present study attempted to explore the effects of different tempos of piano music on heart rate and autonomous nervous system during the recovery period after high‐intensity exercise. In addition, the study analyzed the influence of different tempos on the recovery period of athletes to devise methods for accelerating fatigue recovery through piano music.MethodA total of 57 college students majoring in physical education were selected as experimental subjects and were divided into three groups, namely Lento group (n = 20), Moderato group (n = 20), and Allegretto group (n = 20; only 17 students completed the experiment).ResultsUnder the same high‐intensity exercise regimen, the three groups did not differ significantly in the body composition, high‐intensity exercise ability, and time‐domain variation indices, namely heart rate (HR), heart rate variability index parameters (p > .05). The time‐domain variation analysis in the recovery period revealed significant differences in HR frequency domain indices among the groups exposed to different rhythms (p < .05).ConclusionModerate‐tempo piano music was the most effective in facilitating HR and autonomic nervous system recovery during the recovery period.

Mykola Sylvansky’s Cultural and Pedagogical Activities as A Unique Aspect of His Creative Biography

The article focuses on the cultural and pedagogical activity of Mykola Yosypovich Silvanskyi at the general and specialized piano department at the P. I. Tchaikovsky Kyiv Conservatory. The facts of his creative biography are presented on the basis of the studied materials that are stored in the Central Archive-Museum of Literature and Arts of Ukraine also in the archive of the P. I. Tchaikovsky National Music Academy of Ukraine. Knowledges have also been gained due to the interviews with colleagues, students and relatives of M. Silvanskyi. Mykola Yosypovych's compositional work formed the basis of the cultural-pedagogical activity of the artist, because his works are still actively performed both by professional pianists and by students of conservatories at various stages of learning to play the piano. The personal qualities of the artist, which are described in the article, are important for the comprehensive characterization of his cultural and pedagogical activity. The features of his individual performance style are outlined as the features of a pianist with a romantic orientation (this is evidenced by concert programs found in archival posters). The repertoire policy of M. Silvanskyi in the class of general and specialized piano is described, as well as its inextricable relationship with his stage performance and compositional work. The study determines the factors that form the didactic principles of teaching students to play the piano, leading among which was the principle of practical demonstration of the most effective methods aimed at achieving the desired results (borrowed from Ya. Flier during his studies at the P. I. Tchaikovsky Moscow Conservatory). It is proven that a whole generation of student composers, studying with Mykola Yosypovych in the general piano class, formed artistic and aesthetic values that became leading in their further creative activity and influenced the development of Ukrainian piano music of the late 20th and early 21st centuries. The need for further study and understanding of the significance of M. Silvanskyi's creative activity for a better understanding of the artist's role in the development of Ukrainian musical culture is emphasized.

Intelligent Analysis and Classification of Piano Music Gestures with Multimodal Recordings.

In the traditional recording system, recording any music includes a sizeable instrumental setup and allocates space for the music players. Lighter and fewer devices are replacing larger instruments due to technological advancement and epidemic environmental conditions. This research focuses on text, but audio and video types are also considered. Multiple signal classification with a 5G-based wireless communication network algorithm is implemented to perform the automatic recording and classification of the music data. In this research, a multi-modal gesture recognition dataset is considered for analysis. The dataset was obtained using sensor networks and an intelligent system to record the musical gestures and classify the recorded gestures. The development of machine learning algorithms is not limited to similar technological concepts. Still, it extends to almost all other technical resources such as the 5G network, signal processing, networking, and all other technical resources. This would lead to additional engineering challenges that are utilized in most cases, such as the development of gestures with multi-mode recording. This research has proposed MSA with WCN algorithm to perform intelligent analysis and classification of piano music gestures and is compared with the existing K-Means algorithm and achieved an accuracy of 99.12%.

Influences of Béla Bartók’s Piano Compositions on Alberto Ginastera’s Piano Music

Abstract This study seeks to reveal the musical relationship between Bartók’s and Ginastera’s piano compositions, showing stylistic conceptualizations of twentieth-century Western art music which held many interesting musical aspects, specifically in the classical piano repertoire. This musicological research employs stylistic analysis and theoretical analysis to shed some light on Bartók’s and Ginastera’s piano music vocabulary. Following this methodology, it is revealed that Ginastera’s compositional language was very much influenced by the highly unique characteristics of Bartók’s. Such characteristics include the axial system, Bulgarian peasant rhythmic dance, bi-modality, intervallic cell (Z-cell, 0167), hexachordal sets of octatonic collections, the symmetrical old Hungarian pentatonic scale, polychord, quartal chords, hammering-percussive piano style, secundal chords based on ostinato, and many others. In light of this, one may propose that Ginastera’s compositional strategies in piano music were heavily influenced by Bartók’s rhythmic and harmonic idioms, which contributed to the creation of an innovative musical craft.

Psychological Quality of Piano Players Based on Big Data Algorithm

Piano performance has been around for hundreds of years, and piano performance is currently on the rise. As a pianist, better presentation and performance skills are necessary as a player. And in the process of playing, people must also have a calm psychological quality in order to play the piano perfectly and bring beautiful enjoyment to the audience. This is also the basis for the smooth progress of the performance. The better the psychological quality of the performer, the better the performance will be. This paper analyzes the influence of the psychological quality of piano performance based on the big data algorithm and finds that under the influence of the big data algorithm, the psychological quality of the performers is enhanced by 18%. As for the richness of on-site experience and the stable performance of technology, it is about 20% higher than normal in all aspects, which can provide reference for related research.

Study on Integration and Application of Artificial Intelligence and Wireless Network in Piano Music Teaching.

Until 2019, most people had never faced the situation that would be their life-changing moment. Most universities are conducting classes for their students with the help of virtual classrooms indicating massive technological growth. However, this development does not take enough time to reach the students and the teaching person. Within five to six months of successful projects, most application producers have launched their official sites to conduct online classes and test ways for students. The introduction of virtual classes is not the only example of technological advancement; cloud computing, artificial intelligence, and deep learning have collaborated to produce appropriate, fine, and less error-prone results in all such fields of teaching. These technological advancements have given way to design models created with the wireless networks that are being made, particularly for music-related courses. The Quality-Learning (Q-Learning) Algorithm (QLA) is a pillar study for improving the implementation of artificial intelligence in music teaching in this research. The proposed algorithm aids in improving the accuracy of music, its frequency, and its wavelength when it passes. The proposed QLA is compared with the existing K-Nearest Neighbour (KNN) algorithm, and the results show that QLA has achieved 99.23% accuracy in intelligent piano music teaching through wireless network mode.

"Avec Picasso, ce matin..."

The current music scene is manifold in its means of expression. As music becomes more diverse and embraces other art forms it makes room for a new type of performers to flourish. My academic path, comprising jazz theory lessons and an uncomplete bachelor’s in composition beyond my classical piano studies, gave me the opportunity to develop a set of artistic skills that go beyond the standard performer curriculum. I saw in this artistic research the perfect chance to apply this knowledge. In my work I explore three distinct approaches to a specific piece of music: Constança Capdeville’s Avec Picasso, ce matin… (1984), starting from a more traditional analysis and progressively diverging from this path towards a subversive approach inspired by the concept of anamorphosis. Capdeville’s score is essentially an open work with a set of guiding instructions. However, the lack of documentation, clear instructions and reliable sources concerning the piece made the notated score more ambiguous than expected. This possible setback in its reconstruction turned out to be the perfect context to put my expertise to the test. While dabbling with Capedeville’s score I resorted to experimental processes, to my experience with composition, my transcription and editing skills, archive work and the use of different media in contemporary performance practice. I approached the score as a “script” rather than a “text” (Cook, Nicholas). In this research the score is no longer an end in itself, but rather an excuse towards the development of a musical project larger than it.

GiantMIDI-Piano: A Large-Scale MIDI Dataset for Classical Piano Music

Symbolic music datasets are important for music information retrieval and musical analysis. However, there is a lack of large-scale symbolic datasets for classical piano music. In this article, we describe the creation of the GiantMIDI-Piano (GP) dataset containing 38,700,838 transcribed notes and 10,855 unique solo piano works composed by 2,786 composers. We extract the names of music works and the names of composers from the International Music Score Library Project (IMSLP). We search and download their corresponding audio recordings from the Internet. We further create a curated subset containing 7,236 works composed by 1,787 composers where the titles of downloaded audio recordings contain the surnames of composers. We apply a convolutional neural network to detect solo piano works. Then, we transcribe those solo piano recordings into Musical Instrument Digital Interface (MIDI) files using a high-resolution piano transcription system. Each transcribed MIDI file contains the onset, offset, pitch, and velocity attributes of piano notes and pedals. GiantMIDI-Piano includes 90% live performance MIDI files and 10% sequence input MIDI files. We analyse the statistics of GiantMIDI-Piano and show pitch class, interval, trichord, and tetrachord frequencies of six composers from different eras to show that GiantMIDI-Piano can be used for musical analysis. We evaluate the quality of GiantMIDI-Piano in terms of solo piano detection F1 scores, metadata accuracy, and transcription error rates. We release the source code for acquiring the GiantMIDI-Piano dataset at <a href="https://github.com/bytedance/GiantMIDI-Piano" target="_blank">https://github.com/bytedance/GiantMIDI-Piano</a>.

Empowering Music Students: An Investigation of Psychological Stress and Gender Effects on Piano Performance in Chinese Colleges

In Chinese institutions, piano performance is a well-liked and established area of study. Numerous colleges provide extensive music curricula with a focus on classical piano performance. Traditional musical education, such as music theory and history, as well as actual performance skills, are frequently covered in the curriculum. In addition to examining the effects of psychological stress and gender on piano performance in Chinese colleges, the study focuses on empowering music students and incorporates planned behaviour theory. There are 420 students from several Chinese institutions participated in an online and in-person survey that collected the data for the analysis. According to the findings of structural equation modelling (SEM), interactive music improves student performance. Additionally, the results of the study showed a connection between students' performance and musical congruence, interactive music, psychological stress, and student empowerment. Cross-sectional analysis, close-ended questions, and quantitative research are all used in the current study. In light of this, innovative and engaging teaching strategies are considered crucial in the field of education. Additionally, a lot of universities provide chances for students to take part in performances and contests, giving them useful experience and exposure to the music business. In general, Chinese institutions maintain high standards for piano performance, and many gifted musicians have graduated from these programme to pursue lucrative careers as soloists, accompanists, and music educators.

A perceptual measure for evaluating the resynthesis of automatic music transcriptions

This study focuses on the perception of music performances when contextual factors, such as room acoustics and instrument, change. We propose to distinguish the concept of “performance” from the one of “interpretation”, which expresses the “artistic intention”. Towards assessing this distinction, we carried out an experimental evaluation where 91 subjects were invited to listen to various audio recordings created by resynthesizing MIDI data obtained through Automatic Music Transcription (AMT) systems and a sensorized acoustic piano. During the resynthesis, we simulated different contexts and asked listeners to evaluate how much the interpretation changes when the context changes. Results show that: (1) MIDI format alone is not able to completely grasp the artistic intention of a music performance; (2) usual objective evaluation measures based on MIDI data present low correlations with the average subjective evaluation. To bridge this gap, we propose a novel measure which is meaningfully correlated with the outcome of the tests. In addition, we investigate multimodal machine learning by providing a new score-informed AMT method and propose an approximation algorithm for the p-dispersion problem.

Analysis of Different Keying Modes and Brain Sound Image Establishment Mode in Piano Playing

The piano is known as the king of musical instruments for its rich expressiveness. Pianists get rich emotion with tone control. Touch key mode is the primary method of tone control. The purpose of this paper is to conduct research and analysis on different keying modes and brain-sound image establishment modes in piano performance. In this paper, we first propose a fuzzy mathematical proximity comparison method and an audio feature extraction method. Most of the audio features are derived from speech recognition tasks. They can reduce the original waveform sampling signal, thereby accelerating the machine’s understanding of the semantic meaning of audio. Using comparative analysis method, fuzzy mathematical proximity comparison method, and audio feature extraction as research methods, a model analysis research model based on different key touch methods in piano playing was established, and the relationship between piano key touch and brain sound image was studied. The experimental results of this paper show that after using the research method in this paper, the error rate of the data is controlled within 5%. Compared with previous research methods, the error rate is lower and has certain practical value.

Recognition and Error Correction Techniques for Piano Playing Music Based on Convolutional Cyclic Hashing Method

Music as a sound symbol can express what people think; music is both a form of social behavior and can promote people’s emotional communication; music is also a form of entertainment and can enrich people’s spiritual life. In this paper, we propose a new convolutional recurrent hashing method CRNNH, which uses multilayer RNN to learn to discriminate piano playing music using convolutional feature map sequences. Firstly, a convolutional feature map sequence learning preserving similarity hash function is designed consisting of multilayer convolutional feature maps extracted from multiple convolutional layers of a pretrained CNN; secondly, a new deep learning framework is proposed to generate hash codes using a multilayer RNN, which directly uses the convolutional feature maps as input to preserve the spatial structure of the feature maps; finally, a new loss function is proposed to preserve the semantic similarity and balance of the hash codes, while considering the quantization error generated when the hash layer outputs binary hash codes. The experimental results illustrate that the proposed CRNNH can obtain better performance compared to other hashing methods.

The Use of Artificial Intelligence Combined with Wireless Network in Piano Music Teaching

Artificial intelligence (AI) development gives a new development direction to the traditional education model. The education reform in the intelligent environment is urgent. Under this background, the theory, methods, and characteristics of intelligent music education are expounded based on traditional piano music education. AI and Internet-based smart piano education is introduced. The characteristics of smart piano and its methods of assisting music teaching are analyzed. Moreover, literature review and questionnaire are adopted based on intelligent music education and smart piano theory. With two universities A and B in Nanjing as the research object, the questionnaire is designed to investigate and analyze the related problems of smart piano-assisted piano basic teaching in detail. The results show that smart piano positively improves skill training and psychological training in piano teaching. Piano major learners believe that smart piano can obviously help beginners and people with low piano levels. However, smart piano is not as good as traditional piano for high-level learners. In actual teaching, teachers should use different strategies for different levels of learners. Regarding curriculum quality, teachers have not fully played the role of the smart piano, and the curriculum of smart piano-assisted basic piano teaching still needs improvement. Finally, corresponding countermeasures and suggestions for the development direction of smart piano in piano music education are put forward according to the current situation of the smart piano. The results can reflect the current situation of smart piano-assisted basic piano teaching, provide a reference for researchers in related industries, and promote the related development of smart piano.