Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021)最新文献_第5页

Relatório de Pesquisa NESCoM 2021 NESCoM 2021年研究报告

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19465

L. Costalonga, Marcus Vinicius Marvila das Neves

引用次数: 0

Identificação de Áudio Vocaloid com Support Vector Machines: um Estudo de Caso da Hatsune Miku Vocaloid音频识别与支持向量机:初音未来的案例研究

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19451

F. Almeida, V. Hayashi

引用次数: 0

Production of digital content in music teacher education: a study about podcast’s possibilities 音乐教师教育中数字内容的生产:关于播客可能性的研究

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19448

Marcos Garcia, Gutenberg Lima Marques, Matheus Barros, Juciane Araldi Beltrame

{"title":"Production of digital content in music teacher education: a study about podcast’s possibilities","authors":"Marcos Garcia, Gutenberg Lima Marques, Matheus Barros, Juciane Araldi Beltrame","doi":"10.5753/sbcm.2021.19448","DOIUrl":"https://doi.org/10.5753/sbcm.2021.19448","url":null,"abstract":"This short paper presents an ongoing research that intertwines the theme of educational digital content production for the internet, specifically the audio podcast format, with the pedagogical practices developed in the context of music teacher education and emergency remote teaching. We aim at analyzing the experience of producing digital pedagogical-musical content in the podcast format by students of two Music Education Degree courses. The study uses a qualitative approach and the methodological strategy is based on concepts of action-research. The research is being developed by Technologies and Music Education Research Group (Tedum-UFPB) and by a team of professors from two federal higher education institutions. Data collection will be carried out through the development of field diaries by the research team and through conversation roundtables with the participant students, besides the entire process of documentation, registration and analysis of the phases that make up the action-research cycle. The research presented here can contribute to the processes of creation and conception of audio format content, seeking methodologies that are specific to the musical field, enhancing collective spaces for creation, valuing different authorships and encouraging pedagogical and musical diversity.","PeriodicalId":292360,"journal":{"name":"Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Electric guitar distortion effects unit using a Raspberry Pi 电吉他失真效果单元使用一个树莓派

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19436

Renato Santos Pereira, R. V. Andreão

{"title":"Electric guitar distortion effects unit using a Raspberry Pi","authors":"Renato Santos Pereira, R. V. Andreão","doi":"10.5753/sbcm.2021.19436","DOIUrl":"https://doi.org/10.5753/sbcm.2021.19436","url":null,"abstract":"With the advance of electronics, techniques and algorithms for digital signal processing, digital equipment has been gaining more and more space in the music scene. Micro-processed tools now generate several effects such as modulation, echo, and distortion of sounds generated by musical instruments, previously obtained only by analog units. In this context, this study aimed to develop aprototype of distortion effects unit using a Raspberry Pi (a low-cost small single-board computer) and affordable electronic components. Therefore, five nonlinear functionswere used, four of which are present in the literature andone of them was originally developed by the authors. These functions model the behavior of an active element (suchas transistors, valves, and operational amplifiers), which when they exceed their amplification thresholds produce distortions in the audio signals. Throughout this article, all the steps in the development of the analog circuits for signal acquisition and output will be presented, as well as the simulation and implementation of the functions in the microcontroller. At the end, with the finished prototype, the frequency response analysis is performed and the sound results achieved by the algorithms is compared with each other and with other distortion units.","PeriodicalId":292360,"journal":{"name":"Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114502674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition 通才和特定领域音乐分类模型及其对巴西音乐类型识别的影响

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19427

Diego Furtado Silva, A. Silva, Luís Felipe Ortolan, R. Marcacini

{"title":"On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition","authors":"Diego Furtado Silva, A. Silva, Luís Felipe Ortolan, R. Marcacini","doi":"10.5753/sbcm.2021.19427","DOIUrl":"https://doi.org/10.5753/sbcm.2021.19427","url":null,"abstract":"Deep learning has become the standard procedure to deal with Music Information Retrieval problems. This category of machine learning algorithms has achieved state-of-the-art results in several tasks, such as classification and auto-tagging. However, obtaining a good-performing model requires a significant amount of data. At the same time, most of the music datasets available lack cultural diversity. Therefore, the performance of the currently most used pre-trained models on underrepresented music genres is unknown. If music models follow the same direction that language models in Natural Language Processing, they should have poorer performance on music styles that are not present in the data used to train them. To verify this assumption, we use a well-known music model designed for auto-tagging in the task of genre recognition. We trained this model from scratch using a large general-domain dataset and two subsets specifying different domains. We empirically show that models trained on specific-domain data perform better than generalist models to classify music in the same domain, even trained with a smaller dataset. This outcome is distinctly observed in the subset that mainly contains Brazilian music, including several usually underrepresented genres.","PeriodicalId":292360,"journal":{"name":"Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122206774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An interplay between genre and emotion prediction in music: a study in the Emotify dataset 音乐中体裁和情感预测之间的相互作用:Emotify数据集的研究

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19421

Leonardo Vilela de Abreu Silva Pereira, T. Tavares

{"title":"An interplay between genre and emotion prediction in music: a study in the Emotify dataset","authors":"Leonardo Vilela de Abreu Silva Pereira, T. Tavares","doi":"10.5753/sbcm.2021.19421","DOIUrl":"https://doi.org/10.5753/sbcm.2021.19421","url":null,"abstract":"Automatic classification problems are common in the music information retrieval domain. Among those we can find the automatic identification of music genre and music mood as frequently approached problems. The labels related to genre and mood are both generated by humans, according to subjective experiences related to each individual’s growth and development, that is, each person attributes different meanings to genre and mood labels. However, because both genre and mood arise from a similar process related to the social surroundings of an individual, we hypothesize that they are somehow related. In this study, we present experiments performed in the Emotify dataset, which comprises audio data and genre and mood-related tags for several pieces. We show that we can predict genre from audio data with a high accuracy; however, we consistently obtained low accuracy to predict mood tags. Additionally, we tried to use mood tags to predict genre, and also obtained a low accuracy. An analysis of the feature space reveals that our features are more related to genre than to mood, which explains the results from a linear algebra viewpoint. However, we still cannot find a music-related explanation to this difference.","PeriodicalId":292360,"journal":{"name":"Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121626579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instance Selection for Music Genre Classification using Heterogeneous Networks 基于异构网络的音乐类型分类实例选择

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19419

A. Silva, Paulo Viviurka Do Carmo, R. Marcacini, D. F. Silva

引用次数: 2

Evaluating the Automatic Chord Estimation and Alignments tasks needs using metrics from a code challenge 评估自动和弦估计和对齐任务需要使用来自代码挑战的度量

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19425

Valter Jorge da Silva, G. Cabral

引用次数: 0

Design process and rapid prototyping of animated music visualizations 动画音乐可视化的设计过程和快速原型

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19435

Horhanna Almeida, G. Cabral, Rute Moura

引用次数: 0

Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras 巴西音乐市场的火爆:物理和数字时代的比较

Anais do XVIII Simpósio Brasileiro de Computação Musical (SBCM 2021) Pub Date : 2021-10-24 DOI: 10.5753/sbcm.2021.19440

Gabriel R. G. Barbosa, Bruna C. Melo, Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, M. Moro

引用次数: 7