Machine Learning Applied to Music/Audio Signal Processing

Lerch, Alexander; Knees, Peter

doi:10.3390/electronics10243077

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Calvo-Zaragoza et al (2021) examined the application of optical music recognition (OMR) to transform digital audio data into non-digital audio data. Lerch and Knees (2021) investigated the new approaches in the field of music information retrieval and audio signal processing new approaches in the field of music information retrieval and audio signal processing, mainly through machine learning solutions. Koelsch and Jäncke(2015) proposed a new assessment method to measure heart rate changes associated with music and other factors.…”

Section: Musicmentioning

confidence: 99%

A Literature Review on Music Parameter Extraction and Visualization

Jin,

Zairul,

Salih

2024

IJARBSS

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Music visualization research is extremely complex and dynamic. Several researchers have applied various methods to persevere in the study of all aspects that make up music. The complexity of music also includes factors such as waveform, frequency, pitch, rhythm, tempo, timbre, and chords. Researchers in recent years have studied the extraction of single elements, visualization, or cross-discipline for these aspects. As far as the current research is concerned, most of the disciplines related to music visualization are focused on computers, psychology, sports science, and other related disciplines. Research on the elements of music itself has focused on music visualization, music element extraction, music association, music emotion, and the study of several important aspects of music, such as waveform, frequency, pitch, rhythm, tempo, timbre, and chord. After reviewing the research, this paper has found that with the continuous development of science and technology, music visualization has a progressive intersection with computer science, artificial intelligence, and neural networks. Thus, future research can continue to interact more with computer science.

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Section: Musicmentioning

confidence: 99%

A Literature Review on Music Parameter Extraction and Visualization

Jin,

Zairul,

Salih

2024

IJARBSS

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“…In that case, the partition determines the hit count of the fourth partition length; the lower seven bits indicate the number of SMTPE frames, while the second octet indicates the number of SMTP frames. Volume is the smallest unit of time in a MIDI music file (Lerch & Knees, 2021).…”

Section: Liumentioning

confidence: 99%

Trends in Informatization of Electronic Music Composition Data in the Context of Distance Education

Liu

2023

ICST Transactions on Scalable Information Systems

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INTRODUCTION: With the development of information technology in education, many educational institutions and companies have flocked to the emerging online education market. MOOC is a new method of e-learning that has changed the world and significantly impacted the educational community.OBJECTIVES: This paper aims to present typical features for developing an online music education platform using MOOC music, AI-based music learning software, and interactive real-time music learning.METHODS: This paper discusses different models of music teaching methods and pedagogy and summarizes the advantages and disadvantages of such models.RESULTS: Online music education is an internet technology that fully uses the Internet right to organize good music education.CONCLUSION: Based on the characteristics and content of music education, this paper presents possible suggestions for the sustainable and healthy development of new data and information models for music education on the Internet in the future.

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“…Técnicas de aprendizado de máquina tem sido muito empregadas para resolver problemas de processamento digital 1 https://www.musixmatch.com/pt-br 2 https://www.soundhound.com/soundhound 3 https://www.spotify.com/br/ 4 https://scorecloud.com/portugues/ de sinais de áudio e música [2] e as aplicações desses tipos de técnicas se estendem por vários subcampos de pesquisa explorados no MIR. Dentre elas, podemos citar a utilização de uma rede conjunta com Residual Convolutional Block Attention Module (Res-CBAM) para extrair a melodia contida em sinais de música polifônica [3].…”

Section: Introductionunclassified

Detecção de Onsets em Notas de Músicas Instrumentais de Piano utilizando Representação Pitch e Aprendizado de Máquina

Silva,

Araujo,

Pinagé

et al. 2023

Anais Do XLI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Onsets são os instantes de tempo de início dos eventos musicais em sinal de música. A sua detecção automática serve de base para diversas aplicações tais como transcrição automática de música de um ou mais instrumentos musicais, alinhamento de áudio com score e estimação do tempo da música. Este artigo descreve um sistema de detecção automática de onsets em sinais musicais de piano usando a representação tempo-frequência pitch juntamente com classificadores SVM (Support vector machine), Gradient boosting ou CNN 1D (Onedimensional convolutional neural network). Os dois primeiros possuem desempenho similares no conjunto de dados BS1. O Gradient boosting apresenta maior sensibilidade, mas com mais detecções espurias.Palavras-Chave-Recuperação de informação, SVM, boosting, CNN 1D.

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Machine Learning Applied to Music/Audio Signal Processing

Abstract: Over the past two decades, the utilization of machine learning in audio and music signal processing has dramatically increased [...]

Cited by 5 publications

References 15 publications

A Literature Review on Music Parameter Extraction and Visualization

A Literature Review on Music Parameter Extraction and Visualization

Trends in Informatization of Electronic Music Composition Data in the Context of Distance Education

Detecção de Onsets em Notas de Músicas Instrumentais de Piano utilizando Representação Pitch e Aprendizado de Máquina

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