Digital music has various characteristics, such as melody, rhythm, timbre, and harmony. According to these characteristics, music can be classified using artificial intelligence (AI). Music can ...reduce cognitive dissonance and improve memory in humans; however, occasionally, dissonant music can cause negative effects, such as aggravating depression. Therefore, music can be classified using technical methods and used selectively for human mood regulation, sleep improvement, disease relief, and treatment. Herein we present a survey of the fields of music, AI, and health to shed light on the digitization of music. In this survey, we (1) summarize the various characteristic elements of music, such as melody, rhythm, timbre, and harmony; (2) discuss the role of neural networks in classifying music based on these musical characteristics; (3) summarize the positive and negative effects of music with respect to five areas: sleep, memory, attention, mood, and movement; (4) summarize the therapeutic effect of music intervention with respect to various illnesses; and (5) present the future of music therapy as well as provide a few suggestions with respect to music therapy.
The current hotspots of empirical analysis of piano performance skills mainly focus on the recognition of single notes, and there are some limitations in recognition accuracy and noise resistance ...performance. In this paper, to address this problem, firstly, on the basis of big data, we propose to realize the segmentation of the music section and noise section based on the single-port limit energy difference method and perform note onset and stop detection for the music section based on LMS adaptive filtering algorithm, using the musical characteristics of piano to identify the energy jumping point, which effectively improves the accuracy of note onset and stop detection and avoids the situation of missing and wrong diagnosis. Then the piano piece was played as an example, and the scientific evaluation of the piano performance skills was made based on the results of the determination of note types. The results showed that the errors of the eight notes of the piece were 0.9%, 0.30%, 0.24%, 0.28%, 0.34%, 0.11%, 0.63% and 0.28%. The correct rate of determining the types of notes in the performance technique of the music was 100%, and the error of determining all notes was controlled within 1%. This study provides a reference standard for evaluating the quality of music performance and has broad application prospects in the fields of family leisure, music tutoring, etc.
High school string students from one South-Central Texas district were studied. Purposes were: (1) to describe the musical backgrounds and self-efficacy beliefs of string students (N=101), (2) to ...measure the relationship between string playing self-efficacy and achievement (n=65), and (3) to describe the practice behaviors and strategies of high versus low self-efficacy string students (n=16). Descriptive questions included whether or not students took private lessons, started in public school, and how much students practiced. Sixty-five of the 101 chose to audition for their All-Region orchestra. A significant relationship between self-efficacy scores and performance rankings was found. The 8 higher self-efficacy students tended to use more cognitive practice strategies than the 8 lower self-efficacy students. If musical self-efficacy is related to musical achievement and more cognitive practice, music educators should have a better understanding of musical self-efficacy, how it can influence practice, and how aspects of students’ musical backgrounds may influence it.
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