As computer science advances, it intersects intriguingly with the realm of music acoustics, particularly in enhancing piano performance through technological means. This paper delves into an ...innovative approach to piano learning and creation, focusing on emotional expression’s nuances. We have devised a system capable of precise musical tone recognition and sound quality evaluation by adopting Mel Frequency Cepstral Coefficients (MFCC) for the nuanced extraction of piano sounds and integrating dynamic fuzzy neural networks. Our findings show an impressive accuracy rate, with musical tone misidentification below 2.58% and sound quality assessment errors within a 5% margin. This work not only sets a new benchmark in piano performance analysis but also paves the way for revolutionary teaching methods in music education, with profound implications for artistic instruction and emotional expression.
Generally, polyphonic piano music transcription systems are designed to estimate and determine pitch activities along with various note states for each audio frame. While the music transcription ...system has multiple uses in the Music Information Retrieval (MIR) field, due to the complicated structures of the note events, precisely predicting various note states is still regarded as a challenging task. Accordingly, approaches to designing neural network architectures have evolved to facilitate the joint prediction of each note state. However, recent models have not been able to efficiently exploit mutual correlations among different note states. The key contribution of our work is that we verified mutual correlations between the different note states and reflected them in the model architecture. It enables the transcription system to recognize clearer note events and produce high-quality real-world results. We propose a kernel-sharing feature extractor module for exploiting those mutual correlations in the feature extraction step. Moreover, to make a system recognize the shape of the pitch envelope, we added some connections between the note state-specific detector modules in the note state detection step. The efficacy of our architecture was thoroughly validated in a series of experiments using the publicly available MAESTRO datasets proposed by Google Magenta. Furthermore, ablation studies are performed to demonstrate notions of those mutual correlations and show the impact and significance of the suggested approach.
The test results show that the fast Fourier process with multiple time superposition and a dimension length of 40 is most beneficial to the accuracy of the model. The loss curve value of the ...convolutional recurrent network model (CRN) is much lower than the other three models. The music tone recognition model learns better. The accuracy rate value and recall rate value of the CRN are the highest, and the accuracy rates of the four music tone indicators are 94.6%, 92.4%, 93.5%, 92.5%, and the recall rates were 93.2%, 94.9%, 95.2%, and 88.6% respectively; the improved algorithm was the most accurate in terms of F1 values and is suitable for use in vocal music teaching courses. The results show that the algorithm can be broadly performed in the zone of music tone recognition and has a certain contribution to the development of the field of music tone recognition.
The computer-assisted music composition is an active research area since mid-1900. In this paper, we have applied the VOGUE model for designing musical sequence of bandish notations of raga Bhairav, ...a classical Indian music. Variable Order and Gapped hidden Markov model for unstructured elements can capture variable length dependencies with variable gaps in sequential data. In most of raga pattern, a particular pattern repeats itself which may be separated by variable length gaps. VOGUE mines the frequent patterns in raga having different length gaps. These mined patterns are used to model VOGUE for Indian music ragas. Furthermore, we analyzed the benefits of VOGUE model over the standard HMM. To the best of author’s knowledge, this is the very first attempt to model Indian classical music with variable order gapped HMM.
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The interactive artwork on iPad unifies various colors and musical pitch. Ordinary people, particularly children, are able to play imaginatively to unify those two. Even in artistic field, the ...composers with synaesthesia like Scriabin, Messiaen and so on, created some pieces and concept models of instruments. This application aims to two experience for user, the one is to provide the artistic experience of the great composers, the other is provide the free combinations color and pitch to the ordinary user. It is developed on iPad by Swift language.
Hilbert space method is an old mathematical theoretical model developed based on linear algebra and has a high theoretical value and practical application. The basic idea of the Hilbert space method ...is to use the existence of some stable relationship between variables and to use the dynamic dependence between variables to construct the solution of differential equations, thus transforming mathematical problems into algebraic problems. This paper firstly studies the denoising model in the process of music note feature recognition based on partial differential equations, then analyzes the denoising method based on partial differential equations and gives an algorithm for fused music note feature recognition in Hilbert space; secondly, this paper studies the commonly used music note feature recognition methods, including linear predictive cepstral coefficients, Mel frequency cepstral coefficients, wavelet transform-based feature extraction methods and Hilbert space-based feature extraction methods. Their corresponding feature extraction processes are given.
The Spatial-Numerical Association of Response Codes (SNARC) suggests the existence of an association between number magnitude and response position, with faster left-key responses to small numbers ...and faster right-key responses to large numbers. The attentional SNARC effect (Att-SNARC) suggests that perceiving numbers can also affect the allocation of spatial attention, causing a leftward (vs. rightward) target detection advantage after perceiving small (vs. large) numbers. Considering previous findings that revealed similar spatial association effects for both numbers and musical note values (i.e., the relative duration of notes), the aim of this study is to investigate whether presenting note values instead of numbers causes a spatial shift of attention in musicians. The results show an advantage in detecting a leftward (vs. rightward) target after perceiving small (vs. large) musical note values. The fact that musical note values cause a spatial shift of attention strongly suggests that musicians process numbers and note values in a similar manner.
Songs play a vital role in our day to day life. A song contains basically two things, vocal and background music. Where the characteristics of the voice depend on the singer and in case of background ...music, it involves mixture of different musical instruments like piano, guitar, drum, etc. To extract the characteristic of a song becomes more important for various objectives like learning, teaching, composing. This project takes song as an input, extracts the features and detects and identifies the notes, each with a duration. First the song is recorded and digital signal processing algorithms used to identify the characteristics. The experiment is done with the several piano songs where the notes are already known, and identified notes are compared with original notes until the detection rate goes higher. And then the experiment is done with piano songs with unknown notes with the proposed algorithm.
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Execution time in hitting instrument buttons in human play was identified using time-frequency analysis and peak detection to define time range which can be tolerated as time value that not too fast ...or not too late in hitting buttons, and then the result of the analysis was used as parameters to randomize approximate time to play a note. ...automatically hitting an instrument button to play a note should not be executed as exact as its time target but it should refer to human play that hits an instrument button based on an approximate time. Peak detection is to identify a time value when human hitting an instrument button, and then the time value was calculated based on a time target to set a tolerated range of time (RT) used for randomizing approximate time (AT) to play a note. 2.Research Method Natural automatic musical notes player was developed by analyzing the way a musician estimating execution time referred to the time target of the tempo. The analysis was conducted by performing fast fourier transform (FFT) technique to remove noise, and then followed by performing peak detection technique to find the exactly time value of an approximate time from the gamelan musician play.
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