Anais do Simpósio Brasileiro de Computação Musical (SBCM)
Sociedade Brasileira de Computação - SBC
Automatic classification of instruments from supervised methods of machine learning
Sorting instruments is not an easy task for humans or computers, especially when it comes to elements with the same acoustic properties, such as wind, percussion, or strings. Nevertheless, the use of audio descriptors and artificial intelligence techniques can make this duty more accessible. In this paper, three supervised methods, Naive Bayes, decision tree and Support Vector Classifier (SVC) are used to categorize acoustic guitar and bass sounds in a database, using as a parameter the information extracted from audio descriptors. The research resulted in a performance comparison of these three algorithms, considering their hit rates and processing time when classifying samples in different parts of the dataset. After all, some relevant considerations about the feasibility of automatically classifying instruments are presented.
Instance Selection for Music Genre Classification using Heterogeneous Networks
In scenarios involving musical data, there are usually high-dimensional data and different modalities, such as audio and text, that cost more in machine learning tasks. Instance selection is a promising approach as pre-processing step to reduce these challenges. With the intent to explore the multimodality in music information, we introduce musical data instance selection into heterogeneous network models. We propose and evaluate ten different heterogeneous networks to identify more representative relationships with various musical features related, including songs, artists, genres, and melspectrogram. The results obtained allow us to define which network structure is more appropriate considering the volume of available data and the type of information that the features have. Finally, we analyze the relevance of the musical features, and the relationship does not contribute for instance selection.
Tempo estimation via neural networks - a comparative analysis
This paper presents a comparative analysis on two artificial neural networks (with different architectures) for the task of tempo estimation. For this purpose, it also proposes the modeling, training and evaluation of a B-RNN (Bidirectional Recurrent Neural Network) model capable of estimating tempo in bpm (beats per minutes) of musical pieces, without using external auxiliary modules. An extensive database (12,333 pieces in total) was curated to conduct a quantitative and qualitative analysis over the experiment. Percussion-only tracks were also included inthe dataset. The performance of the B-RNN is compared to that of state-of-the-art models. For further comparison, a state-of-the-art CNN was also retrained with the same datasets used for the B-RNN training. Evaluation results for each model and datasets are presented and discussed, as well as observations and ideas for future research. Tempo estimation was more accurate for the percussion-only dataset, suggesting that the estimation can be more accurate for percussion-only tracks, although further experiments (with more of such datasets) should be made to gather stronger evidence.
An interplay between genre and emotion prediction in music: a study in the Emotify dataset
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.
Mandrit: a system for automatic generation of musical visualizations for rhythm analysis
Faced with the representational challenge of visually communicating musical information, we developed Mandrit: a system for automatic generation of musical visualizations for rhythm analysis. It uses data extracted from MIDI files to plot graphs for different songs, representing their rhythmic signatures. The system is customizable and presents new ways to visualize pace through different metrics. We aim to encourage the discussion in this field and the development of tools to generate musical visualizations, as a means to help musicians and scholars in musical analysis, teaching, composing and complementing musical performances.
On the Fusion of Multiple Audio Representations for Music Genre Classification
Music classification is one of the most studied tasks in music information retrieval. Notably, one of the targets with high interest in this task is the music genre. In this scenario, the use of deep neural networks has led to the current state-of-the-art results. Research endeavors in this knowledge domain focus on a single feature to represent the audio in the input for the classification model. Due to this task’s nature, researchers usually rely on time-frequency-based features, especially those designed to make timbre more explicit. However, the audio processing literature presents many strategies to build representations that reveal diverse characteristics of music, such as key and tempo, which may contribute with relevant information for the classification of genres. We showed an exploratory study on different neural network model fusion techniques for music genre classification with multiple features as input. Our results demonstrate that Multi-Feature Fusion Networks consistently improve the classification accuracy for suitable choices of input representations.
Towards Real-time Score Analysis in PWGL
In this paper, we introduce an original approach to computerized music analysis within the graphical computer-assisted composition environment called PWGL. Our aim is to facilitate the realtime analysis of interactive scores written in common Western music notation. To this end, we have developed a novel library that allows us to analyze scores realized with the help of ENP (the graphical music notation module of PWGL), and to visualize the results of the analysis in realtime. ENP is extended to support the display of supplementary information that can be drawn on top of the score as an overlay. The analysis backend is realized with the help of our builtin musical scripting language based on pattern matching. The analysis results are presented directly as a part of the original score leveraging the extensible and interactive visualization capabilities of ENP. In this paper we describe the current state of the library and present, as a case study, a fully functional application allowing for the realtime analysis and display of voice leading errors according to the counterpoint rules developed mainly in the Renaissance and Baroque eras.
Evaluating the Automatic Chord Estimation and Alignments tasks needs using metrics from a code challenge
Automatic Chord Estimation is a subject of Music Information Retrieval who tries to extract the chords of a song in an usable manner. In the last year, many reseachers tried to overperform the quantitative metrics, but the results lack reprodutibility by who needs them, musicians. In this article, we reviewed the state of art of some of this areas and performed a code Challenge who was evaluated by some of the MIREX metrics and by musicians. Then, with this results, we evaluated the need of evolution on the Estimation task and on the Alignment Task of the MIR area.
Training a convolutional neural network for note onset detection on the clarinet
Although computational models for note onset detection have improved drastically in the last decade, mainly due to the advances brought by the field of Deep Learning, such models have not been perfected yet. When dealing with specific data, like clarinet recordings, those models still produce a significant number of false positives and negatives. In this paper, we evaluate pre-trained onset detection models from the library madmom on a dataset composed of solo clarinet recordings, in particular, to investigate their performance on this kind of data. Moreover, we use the clarinet dataset to train the same neural network (CNN) employed in one of those models, to investigate whether training the model on this specific data leads to an improvement when dealing with clarinet recordings. The results obtained from the model trained strictly on clarinet data are considerably better than those from models trained on generic data.
On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition
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.
Applications of FFT for timbral characterization in woodwind instruments
The conceptualization of the musical timbre, which allows its quantitative evaluation in an audio record, is still an open-ended issue. This paper presents a set of dimensionless descriptors to assess the musical timbre of woodwind instruments in recordings of the fourth octave of the tempered musical scale. These descriptors are calculated from the Fast Fourier Transform (FFT) spectra using the Python Programming Language, specifically the SciPy library. The characteristic spectral signature of the clarinet, bassoon, transverse flute, and oboe are obtained in the fourth musical octave, observing the presence of degeneration for some musical sounds, that is, two given different aerophones may present the same harmonics. It is concluded that the proposed descriptors are sufficient to differentiate the aerophones studied, allowing their recognition, even in the case that there present the same set of harmonic frequencies.
Real-time Qualification of Percussive Sounds Based on Correspondences Between Schaeffer’s Solfège and Low-level Audio Descriptors
Pierre Schaeffers typomorphology (1966) proposes seven criteria of musical perception for the qualification of sound objects, which form the basis of his musical theory. This Solfège fits well into contexts where pitch is not the dominant dimension. Relying on similarities between the practice of reduced listening and the utilization of low-level audio descriptors, we present the first version of a real-time setup in which these descriptors are applied to qualify percussive sounds. The paper describes the tools and strategies used for addressing different criteria: envelope followers with different window sizes and filtering; detection of transients and amplitude modulations; extraction and counting of spectral components; estimation of intrinsic dissonance and spectral distribution; among others. The extracted data is subjected to simple statistical analysis, producing scalar values associated with each segmented object. Finally, we present a variety of examples.
A Roughness Model Implementation to Analyze Sound Mass Composition: A Case Study in Ligeti’s Continuum for Harpsichord (1968)
We present in this paper an implementation of a roughness descriptor based on the model of Pantelis Vassilakis. We apply the implemented descriptor to perform a computer-aided musical analysis of György Ligeti’s Continuum for harpsichord (1968). Our analysis establishes a parallel between the roughness model, concepts of sound mass composition, and Ligeti’s ideas of timbre of movement and permeability. As a result, we display a graphical representation for the roughness present in the Continuum’s recording, propose a formal segmentation for the piece, and analyze its formal development from a musicological point of view.
Concatenative Sound Synthesis as a Technomorphic Model in Computer-Aided Composition
The text presents a process aimed at computer-aided composition for percussion instruments based on Concatenative Sound Synthesis (CSS). After the introduction, we address the concept of ”technomorphism” and the influence of electroacoustic techniques in instrumental composition. The third section covers processes of instrumental sound synthesis and its development in the context of Computer-Aided Composition (CAC) and Computer-Aided Music Orchestration (CAMO). Then, we describe the general principles of Concatenative Sound Synthesis (CSS). The fifth section covers our adaptation of CSS as a technomorphic model for Computer-Aided Composition/Orchestration, employing a corpus of percussion sounds/instruments. In the final section, we discuss future developments and the mains characteristics of our implementation and strategy.
grainBirds: Evolutionary granular synthesisers distributed in Fog Computing
The article presents the grainBIRD concept inspired by a distributed fog of grain sounds metaphor. Shortly, granular synthesizers are distributed in a mobile devices network. Applying Interactive Genetic Algorithm (IGA) and exchanging Open Sound Control (OSC) messages in a Virtual Private Network, grainBIRDs generate sound grains in standalone and network configurations. The grainBIRDs concept dialogues with the Internet of Things (IoT) paradigm since it was inspired by Cloud and Fog computing architectures. In this sense, this article presents the grainBIRD and grainBIRD Orchestra concepts, followed by a review on Computer Music and Network technologies related to the project. The article also discusses the computer implementation of the grainBIRD application using the Pure Data programming environment and concludes with performance tests of the network communication feasibility and the system sound generation capacities.
Correlating Big Five Primary Personality Dimensions with Musical Preferences
According to J Clement in 2019 an estimated 2.95 billion people were using social media worldwide, with a projection of 3.43 billion in 2023. Social networks increasingly play a fundamental role in the way of expressing our-selves and relating to others, in which it is directly linked to our personality. Several studies relate a persons personality to the most diverse areas, such as predicting political orientation, classifying gender, or even measuring the level of success at work. Wu Youyou, 2015 proposes an algorithm that can allegedly estimate the personality of a Facebook user better than his partner, based on his digital footprint, thanks to AI techniques and the massive number of inter-actions that users perform on their social media. The focus of this work is to relate the Big Five personality theory with musical preferences. Since social media posts register moments and impressions of users lives, and since music streaming platforms provide material about musical taste second Peter J RentFrow et al, 203, we used tools to extract the big five primary personality dimensions from Facebook and audio features from the Spotify Web API, and looked for correlations be-tween musical preferences and personality. Results show some correlations and anticorrelations found, pointing to further research with a broader range of features.
Motus 4: new developments on a web-deployed software for composing real-time audiovisual interactions
Camera-based audiovisual interactions have been greatly used in art installations. For such, it is often necessary to use specialized hardware and software. This restricts audiovisual installations to areas that can both afford them financially and be physically accessed by the audience. In this work, we present the development of a web-deployed composition tool for audiovisual interactions that runs on the client side and does not require installing any additional software. The tool allows quickly configuring areas for movement capture, and customizing the corresponding audio feedback. Simultaneously, it provides visual feedback that can aid the audience to understand the experience. Consequently, the tool can be used to compose audiovisual interactions that reach a large audience via web. Moreover, its virtual and ubiquitous nature can foster a diversity of artistic explorations related to the remote characteristic of the relationship between audience and artwork.
Design process and rapid prototyping of animated music visualizations
The production of animations for Musical Information Visualization is still scarce and has challenges in the way of visually communicating information. Due to the need for domains of editing software, demanding technical skills and specific knowledge related to each area: animation, music, design and computing. In this article, we present a systematic review of the animated visualization area and, based on its conception processes, we elaborate an experimental model for the creation, prototyping and construction of musical animations. And through sessions developing quick prototypes, where we obtained qualitative results with feedback collection. We conclude that the importance of animation is exceptional, as an ally in the process of designing and creating a musical visualization, as it facilitates the representation of time to communicate structural elements of music, as they are dynamically arranged in a graphic area.
Electric guitar distortion effects unit using a Raspberry Pi
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.
Challenges to generate musical visualizations
The technological acceleration of recent years has allowed for significant advances in data processing and the study of Music, an art with richly expressive and communicative information. In this article, we bring reports of experiences and contributions in the treatment of musical data extracted from digital MIDI and sound files, for the development of systems that generate musical visualizations.
A preliminary study of Augmented Musical Instruments for Study (AMIS) using research through design
The emergence of Digital Musical Instruments (DMIs) in the computer music field has been providing new means of interaction with music performances. Particularly, with the advancements in the Internet of Things (IoT) area, there has been an increase in augmented musical instruments containing LEDs within their own bodies to support music learning. These instruments can help musicians by having the capabilities to display musical notes, chords and scales. This research uses research through design to present a preliminary study of some of the challenges related to the design of these instruments and attempts to provide some insights associated with their usability and development, particularly related to the development of an augmented acoustic guitar, the VioLED, developed alongside the company Daccord Music. The system provides three modes of operation: song mode, solo/improvising mode and animation mode. The preliminary results present challenges and obstacles pertinent to usability and development of these instruments identified in different iterations of design. These challenges are associated with areas such hardware-software co-design, usability, latency/jitter, energy consumption, etc.
CatalogToMakers: a collaborative cataloging platform for electronic components and physical computing projects related to musical creativity
The Maker Movement is a growing reality in the new industry and academic community. It consists of the Do-It-Yourself (DIY) style-spreading proposal, emphasizing the creation and development of creative thinking. In this context, the democratization of the Internet has meant that non-expert users are capable of developing new hardware technologies. In the music context, the easy access to low-cost equipment and the large number of people sharing knowledge facilitate prototyping and extend the creation of digital musical instruments. However, the places where the information related to the components used in these projects are not centralized nor organized. Therefore, it makes access to this information difficult, slowing down and hindering the flow of creative development. Thus, to reduce this development time and facilitate access to this information, this paper describes the processes for designing the CatalogToMakers tool, a collaborative cataloging platform for electronic components, and physical computing projects focusing on musical creativity. This approach implements several essential functions to facilitate the conception process of hardware development. Finally, an evaluation focused on non-expert users showed promising results.
Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras
Consuming music through streams has made huge volumes of data available. We collect a part of such data and perform cross-era comparative analyses between physical and digital media for successful artists within the music market in Brazil. Given an artist’s career, we focus on hot streak periods defined as high-impact bursts occurring in sequence. Specifically, we construct artists’ success time series to detect and characterize hot streak periods for both physical and digital eras. Then, we assess their features, analyze them in the genre scale, and perform a cluster analysis to identify groups of artists with distinct success levels. For both physical and digital eras, we find the same clusters: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our insights shed light on significant changes in the dynamics of the music industry over the years, by identifying the core of each era.
The 4E‘s Model of Enactivism through Improvisation within DMIs
This paper describes how a theoretical framework focused on the 4E‘s model which describe the mind as fundamentally: embodied, embedded, extended and enactive, within the paradigm of enactive music cognition can contribute to the design of Digital Music Instruments (DMIs). From an epistemological perspective, we discuss improvisation within the Western academic music culture through two examples of DMIs created to improvise in a MIDI keyboard. We argue the 4 E‘s model orientation revealing the fundamentally nature of the embodied musical mind. Ethical and practical possibilities for an enactive music cognition related to improvisation in the context of the skills and needs of 21st are suggested with the goal of helping DMIs designers and musicians to develop approaches based in possibility, imagination, and relationality, rather than in conformity to standardized practices and conventional music pedagogical purposes. Finally, we present some concrete cases of DMIs, and we describe how the experience of musical improvisation with them may be seen through the prism of such theories.
An open source platform to assist the creation of group playlists through artificial intelligence algorithms
Recommendation systems are a constantly expanding study area, with applications in various fields such as e-commerce, films, music to promote the user’s suggestions. When we talk about music, we have more than 20 years of studies trying to solve the problem of a good generation of playlists that maximizes the satisfaction of a larger number of listeners. For automated automatic playlist generation methods focusing on a user group, we have the collaborative filter as a more assertive method to get the user’s not likely, to improve the performance of group recommendation algorithms we store the preferences of users Especially I did not like it by placing the availability of using this data as an algorithm input parameter. The platform described in This paper is intended to facilitate testing between these recommendation systems, standardizing data entry, and facilitating requests. The use of GraphQL as a framework associated with Apollo as a library, greatly facilitates the integration of these APIs, as the separation of data sources makes it possible to associate Spotify data with Deezer or Apple Music data, these data are stored in the database of the connection, so that in future requests it will no longer be necessary to consult the Spotify API, thus facilitating the consumption of data from the artificial intelligence algorithms, as well as a possible sharing of songs between services, since all services have an ISRC code to identify the songs.
Playing Time-Variant Audio Feedback Networks
This article presents practical and artistic contributions to the field of computational musical systems based on audio feedback networks which have been used as instruments for music creation in the author's artistic practice. The article begins with an introduction to the research field of feedback and selforganized music systems. Later on two systems are presented: the first is a network of cross-modulated sinusoidal oscillators (by frequency modulation), and the second is a network of transforming processes of pre-recorded sound samples.
Lick the Toad: a web-based interface for collective sonification
Lick the Toad is an ongoing project developed as a web based interface that runs in modern browsers. It provides a custom made platform to collect user data accessed from mobile devices, such as smartphones, tablets etc. The system offers a tool for interactive collective sonification aiding the idea of networked music performance. It can be used in various contexts, such as onsite installation, interactive compositional tool, or for the distribution of raw data for live coding performances. The system embeds neural network capabilities for prediction purposes by using user input and outputs/targets alike. The inputs and the targets of the training processes can be adapted according to the needs of the use making it a versatile component for creative practice. It is developed as open-source project and it works currently as a NodeJS application with plans for future deployment on remote server to support remote communication and interaction amongst distant users.
Mapping, Triggering, Scoring, and Procedural Paradigms of Machine Listening Application in Live-Electronics Compositions
Since the advent of real-time computer music environments, composers have increasingly incorporated DSP analysis, synthesis, and processing algorithms in their creative practices. Those processes became part of interactive systems that use real-time computational tools in musical compositions that explore diverse techniques to generate, spatialize, and process instrumental/vocal sounds. Parallel to the development of these tools and the expansion of DSP methods, new techniques focused on sound/musical information extraction became part of the tools available for music composition. In this context, this article discusses the creative use of Machine Listening and Musical Information Retrieval techniques applied in the composition of live-electronics works. By pointing out some practical applications and creative approaches, we aim to circumscribe, in a general way, the strategies for employing Machine Listening and Music Information Retrieval techniques observed in a set of live-electronics pieces, categorizing four compositional approaches: namely, mapping, triggering, scoring, and procedural paradigms of application of machine listening techniques in the context of live-electronics music compositions.
Acalma-te: resultados de composição automática e iterativa para relaxamento
A música é uma forma de expressão invisível, não palpável e ainda maleável tornando-se difícil a tarefa de descrevê-la, rotulá-la ou construí-la. A base de qualquer música é composta por o ritmo, melodia e harmonia. Este trabalho apresenta uma composição intitulada de Acalma-te, resultado do projeto experimental de geração de músicas para relaxamento. O compositor informa, tonalidades, bpm, quantidade e tipo de estrofes e quantidade de faixas. Pode-se acompanhar o resultado em tempo real permitindo interatividade para mudanças de tonalidades, bpm e qualquer outra simbologia necessária para o compositor. Ao final, é gerado um arquivo MIDI de cada faixa como de todas as faixas podendo adicioná-las em qualquer programa de edição sonora.
Investigando Tecnologias Educacionais para o Ensino e Aprendizagem de Música para Deficientes Visuais Totais
O surgimento de tecnologias assistivas para o ensino de música para cegos trouxe inúmeros benefícios, porém, mesmo com a evolução tecnológica, os diferentes métodos de ensino podem não abranger a totalidade desse grupo. Portanto, se faz necessário conhecer as tecnologias disponíveis, para os pontos de convergência sejam conhecidos e novas funcionalidades sejam propostas. Para essa tarefa, este trabalho tem como objetivo investigar na literatura as tecnologias educacionais que apoiam o ensino e aprendizagem de música para deficientes visuais totais por meio de um mapeamento sistemático da literatura. Este trabalho apresentou um levantamento de tecnologias voltadas para músicos e alunos de música deficientes visuais.
Production of digital content in music teacher education: a study about podcast’s possibilities
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.
Song Emotion Recognition: A Study of the State of the Art
Music is art, and art is a form of expression. Often, when a song is composed or performed, there may be an intent by the singer/songwriter of expressing some feeling or emotion through it, and, by the time the music gets in touch with an audience, a spectrum of emotional reactions can be provoked. For humans, matching the intended emotion in a musical composition or performance with the subjective perceptiveness of different listeners can be quite challenging, in account that this process is highly intertwined with people’s life experiences and cognitive capacities. Fortunately, the machine learning approach for this problem is simpler. Usually, it takes a data-set, from which features are extracted to present this data to a model, that will train to predict the highest probability of an input matching a target. In this paper, we studied the most common features and models used in recent publications to tackle music emotion recognition, revealing which ones are best suited for songs (particularly acapella).
Análise de características acústicas para identificação de triggers em gêneros e áudios musicais distintos
Este trabalho analisa em detalhes uma base pública internacional de áudios musicais acerca das características acústicas presentes nas amostras existentes. Ao todo mil áudios classificados em dez gêneros musicais foram analisados em relação às suas características acústicas, com objetivo de buscar padrões nos triggers presentes nas amostras de cada gênero musical e a quantidade destes para aplicação em estudos subsequentes de ativações cerebrais geradas pela escuta destas músicas. Os resultados mostram que dentre os dez gêneros musicais presentes na base de áudios, dois desses (Disco e Metal) não apresentam número de triggers suficiente para tal aplicação.
Identificação de Áudio Vocaloid com Support Vector Machines: um Estudo de Caso da Hatsune Miku
O processamento de sinais de áudio em conjunto com modelos de aprendizado de máquina tem aplicações em diversas áreas: música, análise forense e análise de fala humana e ruído ambiente. Analisar música de diferentes gêneros pode encorajar investigações interessantes pela comunidade científica, como a investigação de tendências culturais. Este trabalho apresenta uma iniciativa para o processamento de canções vocaloides que ganharam grande popularidade nas redes sociais. Os classificadores Support Vector Machine (SVM) foram treinados em dois experimentos para distinguir canções da vocaloide Hatsune Miku de canções instrumentais e canções de outros vocaloides, apresentando resultados promissores de precisão acima de 80%, que validam a iniciativa.
CREATES - Convolutional Neural Network applied to Identification of Musical Elements in OMR
Optical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we present a comparative study among Convolutional Neural Network (CNN) and state-of-the-art classiers to classify musical symbols. The initial results show that the CNN is promising in this task according to provided information.
Modelo automático de classificação de gêneros musicais amazônicos
A globalização afeta a preferência musical da sociedade atual, que apresenta continua estima por ou gêneros musicais internacionais em detrimento aos nacionais ou locais. A música é dos meios de comunicação utilizados para a construção e organização da estrutura social influenciando o estilo de vida, gostos e convivência interpessoal. Portanto, cada manifestação de gênero musical apresenta função distinta para um ouvinte, como por exemplo, para dançar, festejar, descançar, ajudar na solidão, na tristeza etc. Diversos aplicativos como spotify e soundcloud, utilizam-se de classificadores de gêneros musicais para indicar, prever ou sugerir novas músicas para seus ouvintes. Por diversos motivos, a maioria dos classificadores não possui informações de gêneros musicais regionais. Este trabalho propõem um modelo de classificação automático de gêneros musicais populares amazônicos. Inicialmente, confeccionou-se uma base de dados contendo os gêneros musicais: andino, brega, carimbó, cúmbia, marabaixo, pasillo, salsa e vaqueirada, oriundos da região da Amazônia Legal dos países: Brasil, Guiana Francesa, Venezuela, Colômbia, Equador, Bolívia e Peru. Para a construção da base de dados, extraiu-se diversas características de cada música ao total de 64 parâmetros. Analisou-se os modelos de aprendizado de máquina na qual XGB, KNN, SVM e MLP obtiveram acurácia de 67.62%, 74.12%, 71.35%, 76.13%, respectivamente.
Camomile-ELSE: creating plugins with Pure Data patches and the ELSE library
Camomile is an open source project that turns patches made in the Pure Data programming language into plugins (VST, VST3, LV2 and Audio Unit) so you can load them in any plugin host on Windows, Linux and MacOS. We have a fork of this project that embeds the ELSE library of Pure Data externals into camomile so you dont have to only rely on the limited set of native Pure Data objects. This paper describes the potential of the Camomile project, how it makes the creation of plugins very accessible to people without a background in computer science and also how this process is improved by including the extensive ELSE library. We also offer an overview of similar projects and the technical details of including the ELSE library into the camomile project.
Este artigo tem como objetivo explorar como se deu o início da arte digital, como surgiu a web art, como se produz a web art hoje e quais tecnologias desenvolvidas a partir do ideal artístico oferecem possibilidades para a arte e outras áreas.
Esse artigo aborda uma pesquisa transdisciplinar entre áreas de música, computação, acústica e cerâmica. O projeto foi desenvolvido com a ideia de criar um instrumento composto por diversos smartphones que se comunicam por protocolos de rede gerados em patches criados no pure data em conjunto com a plataforma MobMuPlat. Sendo assim, temos um conjunto de ”vozes”, onde cada celular exerce o papel de um músico abstrato. Esses celulares são acoplados em amplificadores modelados em cerâmica que respondem de formas diversas às frequências tocadas, criando assim, uma camada sonora alterada, compondo o instrumento não só como um amplificador, mas também como efeito, influenciando no timbre final.
Rapid prototyping: using Wizard of Oz to emulate machine learning features for interactive artistic applications
In this paper, we present using the Wizard of Oz method to rapid prototyping machine learning features in interactive artistic applications. Machine learning systems often require time and resources until they were available to be executed. But in the era of agile movement where we have to test as soon as possible and in artistic solutions whose scope initially perhaps be unclear, there is a need for a way to fast testing hypothesis. We also briefly described Ama, a system for adult ballet training at home, which served as a proof of concept of our strategy.
Expressividade de instrumentos musicais digitais - Just push play
A criação de um instrumento musical digital é uma atividade que atrai músicos e pesquisadores há décadas e certamente possui problemas em abertos sobre os quais estas pesquisas tem se debruçado. Certamente, criar um instrumento pode ser uma tarefa simples que consiste em organizar uma interface de forma que ela sirva para fazer som. No entanto, esta interface simples pode estar distante do que consideramos um instrumento musical quando pensamos que gerar som pode não ser a única coisa que buscamos ao tocar um instrumento.
Desafios da Internet das Coisas Musicais
A internet das Coisas Musicais é uma área de pesquisa que pretende levar a conectividade da Internet das Coisas para o campo da música e das artes. Junto com esta tecnologia surge a possibilidade de conexão de diferentes coisas musicais em um ambiente de concerto ou de criação artística que permitiria, por exemplo, a participação do público nestes processos, tanto de maneira presencial, por meio de uma rede local, quanto remoto, por meio da Internet. Neste trabalho trazemos algumas discussões sobre a IoMusT e também suas possibilidades e desafios.
CongAr: uma proposta de instrumento musical digital baseado em congas percussivas
Em recentes anos, é possível observar a proliferação de novos instrumentos musicais digitais (IMD) com diferentes desempenhos e finalidades. Atualmente, os IMD possuem combinação de interfaces de síntese sonora, gestuais e demais sensores com possível incorporação aos computadores. Este trabalho propõem um protótipo de instrumento musical digital para percussão em congas intitulado de CongAr. O CongAr utiliza dois sensores de distância representando as congas e um protótipo de arduino, ou seja, a cada distância captada pelo sensor transforma-se em um golpe musical em algum tambor. O CongAr transmite informações por modo em série, rede de comunicação local ou global em forma sonora ou MIDI. O CongAr é um modelo distribuído performático, que permite ações entre vários participantes que podem tocar em conjunto. Para isso, o CongAr possui um gerenciador de captação de informações para gerar conteúdos. Realizou-se testes com Músicos e não-músicos a partir de um questionário qualitativo apresentando satisfatório resultados de usabilidade, entendimento e comunicação visual.
A prospective report on the research developed at the Laboratory of Audio and Music Technology at USP
This paper presents a concise report on the research developed at the Laboratory of Audio and Music Technology at the EACH-USP. The laboratory was founded in 2011 targeting the areas of music technology, musical acoustics and bioacoustics, strengthening its scope in 2019 to the areas of sound and music computing and audio engineering. Six projects are presented herein, describing their application areas, goals, achievements and perspectives.
O ALICE - Arts Lab in Interfaces, Computers, and Everything Else é um laboratório de pesquisa do Departamento de Computação da Federal University of São João del-Rei. Acolhendo pesquisadores de várias áreas, o ALICE tem desenvolvido pesquisas nas áreas de processos criativos amparados pela tecnologia, música em rede, paisagens sonoras, orquestras de dispositivos e outras áreas em que é possível aliar arte e tecnologia sem que uma destas áreas se coloque acima da outra. Neste artigo, apresentamos nossos desenvolvimentos recentes e lamentos atuais em um ano em que a distância de nossos corpos tenta ser superada pela onipresença das redes de computadores.
CEGeME 2021 - Gestos, Movimentos, Performance musical e Pandemia
O CEGeME - Centro de Pesquisa em Gesto e Expressão Musical é filiado ao Programa de Pós-Graduação em Música da Universidade Federal de Minas Gerais (UFMG), sediado na Escola de Música de Belo Horizonte, Brasil, desde 2008 é dedicado à pesquisa empírica da performance musical, que partem de informação de conteúdo musical extraída de sinais de áudio e da posição espacial tridimensional dos músicos, registradas durante uma performance musical. Com o objetivo de estabelecer uma abordagem musicológica a diferentes aspectos da expressividade musical, investigamos relações causais entre a intenção expressiva dos músicos e a forma como manipulam o material acústico e como se movem durante a execução de uma peça musical. O grupo atraiu postulantes de estudos de diversas áreas de conhecimento e músicos profissionais instigados por questões específicas envolvidas na expressividade musical, tais como, estudos sobre o uso da segunda válvula do trombone baixo moderno para obter um melhor legato, os parâmetros acústicos do filtro-fonte no trompete, as emoções faciais presentes na performance de cantores líricos, os parâmetros acústicos e psicoacústicos associados a diferentes preparações de palheta do oboé, a modelagem espectral do ataque de notas na clarineta, análise do timbre musical baseada em aprendizado de máquina, estudos de parâmetros acústicos e cinemáticos associados à execução da transição de notas em legato na clarineta, análise da articulação na performance de uma peça para clarineta de Debussy, aplicações sonológicas na educação musical, influência da consistência na interpretação na coesão da performance da música de câmara e o desenvolvimento de software na plataforma python criado para dar suporte às demandas da pesquisa conduzida no CEGeME.
COUT-e e práticas itinerantes durante 2020 e 2021
Este artigo apresenta ações em andamento e finalizadas pelo grupo itinerante de COmputacão mUsical e Tecnologias Emergentes (COUT-E) da Universidade Federal do Amapá (UNIFAP) durante o período de 2020 e 2021. Comenta-se sobre os projetos Handy Hear, CongAr, Acalma-te, classificação de ritmos populares da amazônia e sincronização rítmica entre fontes distintas em tempo real.
The NESCoM is a multidisciplinary research centre formed by musicians, engineers and computer scientists. This paper reports the ongoing projects and developments over the last two years, thus it is an update over the research report published in 2019. As a Brazilian research group, with solid international collaboration, we have opted to intercalate the language used to write the report, therefore the 2021 version is written in Portuguese. The main projects developed in these two years timeframe are related to interaction design based on (bio)musicality, robotic music performance, and ubiquitous music. In addition, a strong artistic production is also described. If you are interested to get to know more about the projects, do not hesitate to contact us.
A computação musical tem o papel de facilitar as diversas formas de geração de criatividade artística, informação e inovação através de métodos totalmente diversificados. Atualmente, a computação musical atua por diversas ações unilaterais sobre barreiras de criatividade e tecnológicas. Este minicurso tem o objetivo de apresentar a grande área de computação musical para o público interessado desde a teoria musical, fundamentos eletroacústica e geração de sinais, tópicos atuais de interesse, exemplos por diversas linguagens de programação e ainda comentários sobre diversas atuações em universidades brasileiras através de seus laboratórios de pesquisa.
A computação expande as possibilidades na criação de obras artísticas de música visual, com a intencionalidade da associação algorítmica entre elementos do som e elementos gráficos para geração de visualizações. No entanto, há dificuldades interdisciplinares na exploração destes artefatos, pois as pessoas interessadas na temática nem sempre encontram direcionamentos para construí-las. Sendo assim, essa oficina surge pela necessidade de proporcionar a experimentação dessas potencialidades tecnológicas e tem por objetivo estabelecer um ambiente de criação colaborativa durante o SBCM 2021, propondo experimentação de códigos que integram o processamento de som e processamento e síntese de imagem. Utilizaremos um processo de Design colaborativo, considerando os participantes como protagonistas de seus experimentos pelo mapeamento de suas expectativas e interesses, auxiliados por tutorias e esperando obter como resultado esboços e contribuições experimentais de música visual gerativa.
Towards a Latin American NIME Research Community
In this workshop, we propose the first steps towards consolidating a Latin American (LATAM) New Interfaces for Musical Expression (NIME) Research Community. We aim to start a discussion and generate ideas around shortterm actionable initiatives that a LATAM NIME Research Community could explore in the near future. We will discuss various topics centered around three main areas: (1) The creation of a LATAM NIME network, (2) Exploring LATAM NIME perspectives, and (3) the Next Steps for LATAM NIME. After this activity, we will have a plenary discussion where we will share ideas for action and reflections. Lastly, we list a series of expected outcomes of the workshop, although we acknowledge that many others may emerge during the workshop. We look forward to taking the first steps towards a LATAM NIME Community and the future of this community within and beyond NIME, opening to other communities in LATAM, and stirring policy changes to support the development of our local communities.