Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Improving music genre classification using automatically induced harmony rules

We present a new genre classification framework using both low-level signal-based features and high-level harmony features. A state-of-the-art statistical genre classifier based on timbral features is extended using a first-order random forest containing for each genre rules derived from harmony or chord sequences. This random forest has been automatically induced, using the first-order logic induction algorithm TILDE, from a dataset, in which for each chord the degree and chord category are identified, and covering classical, jazz and pop genre classes. The audio descriptor-based genre classifier contains 206 features, covering spectral, temporal, energy, and pitch characteristics of the audio signal. The fusion of the harmony-based classifier with the extracted feature vectors is tested on three-genre subsets of the GTZAN and ISMIR04 datasets, which contain 300 and 448 recordings, respectively. Machine learning classifiers were tested using 5 × 5-fold cross-validation and feature selection. Results indicate that the proposed harmony-based rules combined with the timbral descriptor-based genre classification system lead to improved genre classification rates

Modeling Interval Relations for Neural Language models

In this study, we explore the use of modellingof pitch intervals and interval relations in pitchwith neural networks. Intervals and their relationsare essential features of music, but in neural net-works, the trend is to use raw data as input andnot to model any higher level aspects of the music.We propose to use Relation Based Patterns (RBP)to integrate intervals (early and mid fusion) andinterval relations (late fusion) into the networkstructure. We observe significant improvementsin pitch prediction for the Essen Folk Song Col-lection for RBP over standard networks, and formixed over unsigned and signed interval represen-tation

Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey

Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.Comment: 36 pages, 5 figures, 4 table

Creative Support Musical Composition System: a study on Multiple Viewpoints Representations in Variable Markov Oracle

Em meados do século XX, assistiu-se ao surgimento de uma área de estudo focada na geração au-tomática de conteúdo musical por meios computacionais. Os primeiros exemplos concentram-se no processamento offline de dados musicais mas, recentemente, a comunidade tem vindo a explorar maioritariamente sistemas musicais interativos e em tempo-real. Além disso, uma tendência recente enfatiza a importância da tecnologia assistiva, que promove uma abordagem centrada em escolhas do utilizador, oferecendo várias sugestões para um determinado problema criativo. Nesse contexto, a minha investigação tem como objetivo promover novas ferramentas de software para sistemas de suporte criativo, onde algoritmos podem participar colaborativamente no fluxo de composição. Em maior detalhe, procuro uma ferramenta que aprenda com dados musicais de tamanho variável para fornecer feedback em tempo real durante o processo de composição. À luz das características de multi-dimensionalidade e hierarquia presentes nas estruturas musicais, pretendo estudar as representações que abstraem os seus padrões temporais, para promover a geração de múltiplas soluções ordenadas por grau de optimização para um determinado contexto musical. Por fim, a natureza subjetiva da escolha é dada ao utilizador, ao qual é fornecido um número limitado de soluções 'ideais'. Uma representação simbólica da música manifestada como Modelos sob múltiplos pontos de vista, combinada com o autómato Variable Markov Oracle (VMO), é usada para testar a interação ideal entre a multi-dimensionalidade da representação e a idealidade do modelo VMO, fornecendo soluções coerentes, inovadoras e estilisticamente diversas. Para avaliar o sistema, foram realizados testes para validar a ferramenta num cenário especializado com alunos de composição, usando o modelo de testes do índice de suporte à criatividade.The mid-20th century witnessed the emergence of an area of study that focused on the automatic generation of musical content by computational means. Early examples focus on offline processing of musical data and recently, the community has moved towards interactive online musical systems. Furthermore, a recent trend stresses the importance of assistive technology, which pro-motes a user-in-loop approach by offering multiple suggestions to a given creative problem. In this context, my research aims to foster new software tools for creative support systems, where algorithms can collaboratively participate in the composition flow. In greater detail, I seek a tool that learns from variable-length musical data to provide real-time feedback during the composition process. In light of the multidimensional and hierarchical structure of music, I aim to study the representations which abstract its temporal patterns, to foster the generation of multiple ranked solutions to a given musical context. Ultimately, the subjective nature of the choice is given to the user to which a limited number of 'optimal' solutions are provided. A symbolic music representation manifested as Multiple Viewpoint Models combined with the Variable Markov Oracle (VMO) automaton, are used to test optimal interaction between the multi-dimensionality of the representation with the optimality of the VMO model in providing both style-coherent, novel, and diverse solutions. To evaluate the system, an experiment was conducted to validate the tool in an expert-based scenario with composition students, using the creativity support index test