Comparision Of Adversarial And Non-Adversarial LSTM Music Generative Models

Algorithmic music composition is a way of composing musical pieces with minimal to no human intervention. While recurrent neural networks are traditionally applied to many sequence-to-sequence prediction tasks, including successful implementations of music composition, their standard supervised learning approach based on input-to-output mapping leads to a lack of note variety. These models can therefore be seen as potentially unsuitable for tasks such as music generation. Generative adversarial networks learn the generative distribution of data and lead to varied samples. This work implements and compares adversarial and non-adversarial training of recurrent neural network music composers on MIDI data. The resulting music samples are evaluated by human listeners, their preferences recorded. The evaluation indicates that adversarial training produces more aesthetically pleasing music.Comment: Submitted to a 2023 conference, 20 pages, 13 figure

16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)

The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc

L-Music: uma abordagem para composição musical assistida usando L-Systems

Generative music systems have been researched for an extended period of time. The scientific corpus of this research field is translating, currently, into the world of the everyday musician and composer. With these tools, the creative process of writing music can be augmented or completely replaced by machines. The work in this document aims to contribute to research in assisted music composition systems. To do so, a review on the state of the art of these fields was performed and we found that a plethora of methodologies and approaches each provide their own interesting results (to name a few, neural networks, statistical models, and formal grammars). We identified Lindenmayer Systems, or L-Systems, as the most interesting and least explored approach to develop an assisted music composition system prototype, aptly named L-Music, due to the ability of producing complex outputs from simple structures. L-Systems were initially proposed as a parallel string rewriting grammar to model algae plant growth. Their applications soon turned graphical (e.g., drawing fractals), and eventually they were applied to music generation. Given that our prototype is assistive, we also took the user interface and user experience design into its well-deserved consideration. Our implemented interface is straightforward, simple to use with a structured visual hierarchy and flow and enables musicians and composers to select their desired instruments; select L-Systems for generating music or create their own custom ones and edit musical parameters (e.g., scale and octave range) to further control the outcome of L-Music, which is musical fragments that a musician or composer can then use in their own works. Three musical interpretations on L-Systems were implemented: a random interpretation, a scale-based interpretation, and a polyphonic interpretation. All three approaches produced interesting musical ideas, which we found to be potentially usable by musicians and composers in their own creative works. Although positive results were obtained, the developed prototype has many improvements for future work. Further musical interpretations can be added, as well as increasing the number of possible musical parameters that a user can edit. We also identified the possibility of giving the user control over what musical meaning L-Systems have as an interesting future challenge.Sistemas de geração de música têm sido alvo de investigação durante períodos alargados de tempo. Recentemente, tem havido esforços em passar o conhecimento adquirido de sistemas de geração de música autónomos e assistidos para as mãos do músico e compositor. Com estas ferramentas, o processo criativo pode ser enaltecido ou completamente substituído por máquinas. O presente trabalho visa contribuir para a investigação de sistemas de composição musical assistida. Para tal, foi efetuado um estudo do estado da arte destas temáticas, sendo que foram encontradas diversas metodologias que ofereciam resultados interessantes de um ponto de vista técnico e musical. Os sistemas de Lindenmayer, ou L-Systems, foram selecionados como a abordagem mais interessante, e menos explorada, para desenvolver um protótipo de um sistema de composição musical assistido com o nome L-Music, devido à sua capacidade de produzirem resultados complexos a partir de estruturas simples. Os L-Systems, inicialmente propostos para modelar o crescimento de plantas de algas, são gramáticas formais, cujo processo de reescrita de strings acontece de forma paralela. As suas aplicações rapidamente evoluíram para interpretações gráficas (p.e., desenhar fractais), e eventualmente também foram aplicados à geração de música. Dada a natureza assistida do protótipo desenvolvido, houve uma especial atenção dada ao design da interface e experiência do utilizador. Esta, é concisa e simples, tendo uma hierarquia visual estruturada para oferecer uma orientação coesa ao utilizador. Neste protótipo, os utilizadores podem selecionar instrumentos; selecionar L-Systems ou criar os seus próprios, e editar parâmetros musicais (p.e., escala e intervalo de oitavas) de forma a gerarem excertos musicais que possam usar nas suas próprias composições. Foram implementadas três interpretações musicais de L-Systems: uma interpretação aleatória, uma interpretação à base de escalas e uma interpretação polifónica. Todas as interpretações produziram resultados musicais interessantes, e provaram ter potencial para serem utilizadas por músicos e compositores nos seus trabalhos criativos. Embora tenham sido alcançados resultados positivos, o protótipo desenvolvido apresenta múltiplas melhorias para trabalho futuro. Entre elas estão, por exemplo, a adição de mais interpretações musicais e a adição de mais parâmetros musicais editáveis pelo utilizador. A possibilidade de um utilizador controlar o significado musical de um L-System também foi identificada como uma proposta futura relevante

Toward Interactive Music Generation: A Position Paper

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations

iJazzARTIST: Intelligent Jazz Accompanist for Real-Time human-computer Interactive muSic improvisaTion

Κάποια από τα κυριότερα χαρακτηριστικά του αυτοσχεδιασμού σε πρότυπα τζαζ εκφράζονται μέσα από τη μουσική συνοδεία. Η συνεργασία μεταξύ ανθρώπων και τεχνητών συστημάτων για την επίτευξη αυτοσχεδιασμού σε πραγματικό χρόνο, υπό το πλαίσιο κοινής παρτιτούρας, αποτελεί ένα ιδιαίτερα ενδιαφέρον αντικείμενο μελέτης για τον τομέα της Ανάκτησης Μουσικής Πληροφορίας. Οι προϋπάρχουσες προσεγγίσεις που αφορούν στη διαδικασία της συνοδείας τζαζ αυτοσχεδιασμού, έχουν παρουσιάσει συστήματα που δε διαθέτουν την ικανότητα συμμόρφωσης με δυναμικά μεταβαλλόμενα περιβάλλοντα, εξαρτώμενα από τα αυτοσχέδια δεδομένα. Η παρούσα πτυχιακή εργασία παρουσιάζει ένα σύστημα συνοδείας, το οποίο διαθέτει την ικανότητα προσαρμογής τόσο στο τζαζ σόλο του μουσικού, όσο και τους περιορισμούς που έχουν προκαθοριστεί από την παρτιτούρα. Ο τεχνητός πράκτορας που αναπτύσσεται για το σκοπό αυτό, αποτελείται από δύο υποσυστήματα, ένα μοντέλο υπεύθυνο για την παραγωγή προβλέψεων που αφορούν το σόλο του μουσικού κι ένα δεύτερο υποσύστημα που παράγει την τελική μουσική συνοδεία, αξιοποιώντας την πληροφορία για τις προθέσεις του σολίστα που παρήγαγε το πρώτο μοντέλο. Και τα δύο προαναφερθέντα μοντέλα έχουν ως σχεδιαστική βάση τα Αναδρομικά Νευρωνικά Δίκτυα. Το σύνολο των δεδομένων που χρησιμοποιήθηκαν στην εκπαίδευση των μοντέλων υποβλήθηκαν σε επεξεργασία πολλών επιπέδων, συμπεριλαμβανομένης της πιθανολογικής βελτιστοποίησης, με στόχο τη διατήρηση και την επαύξηση της χρήσιμης πληροφορίας. Το τελικό σύστημα εξετάστηκε με τη χρήση δύο τζαζ προτύπων, παρουσιάζοντας προσαρμοστική ικανότητα ως προς τους αρμονικούς περιορισμούς, καθώς και ποικιλομορφία, εξαρτώμενη από τον αυτοσχεδιασμό του μουσικού. Τέλος, αναφέρονται κάποιες δυσκολίες που προέκυψαν, όπως επίσης και προτάσεις για περαιτέρω έρευνα.Some of the most essential characteristics of improvisation on jazz standards are reflected through the accompaniment. Given a lead sheet as common ground, the study of the collaborative process of music improvisation between a human and an artificial agent in a real time setting, is a scenario of great interest in the MIR domain. So far, the approaches concerning the jazz improvisation accompaniment procedure, have presented systems that lack the capability of performing the accompaniment generation task while at the same time adapting to dynamically variable constraints depending on new, improvised data. The thesis at hand, proposes a jazz accompaniment system capable of providing proper chord voicings to the solo, while complying with both the soloist's intentions as well as the previously defined constraints set by the lead sheet. The artificial agent consists of two sub-systems; a model responsible for predicting the human soloist's intentions and a second system performing the task of the accompaniment. The latter is achieved by modeling the artificial agent's predictions, after exploiting the information on the expectations of the human agent's intentions, previously calculated by the first model. Recurrent Neural Networks (RNNs) comprise both aforementioned models. The dataset used in the training process has undergone multi-staged processing including probabilistic refinement, aiming to keep and enrich the information which is requisite for the task. The system was tested on two cases of jazz standards, demonstrating ability of compliance with the harmonic constraints. Additionally, output variability depending on the solo improvisation has been indicated. Emerging limitations as well as potential future perspectives are discussed in the conclusion of this work

Learning music composition with recurrent neural networks

Throughout this thesis, we are interested in modeling music composition. To do so, we study the association of music theory concepts with the learning capabilities of recurrent neural networks. Especially, we explore numerical formalizations of music so that our models operate on data containing useful information to decide how to combine particular rhythms and notes in specified musical contexts. The first part introduces the Deep Artificial Composer, a generative model of monophonic melodies. We present a network architecture that considers the temporal and instantaneous relationships between the pitch and duration of notes. By combining Irish with klezmer melodies to train the Deep Artificial Composer, we study how it reacts to heterogeneous data. Furthermore, we show how to generate melodies containing Irish or Klezmer folk musicâs characteristics from our probabilistic model of notes. The second part presents BachProp, a model for the composition of polyphonic music. We discover how scores of different styles can be generated from models using neural networks. In particular, we train BachProp with Bachâs chorales and the English folk music corpus Nottingham. Also, we verify that generated compositions share specific properties with the original corpus it processes. Especially, our musical novelty statistics show that BachProp generates musical sequences as innovative as those present in the original corpora. Finally, a large audience appreciated the Ada string quartet performances of BachPropâs compositions. Whereas BachProp operates on any music in the usual MIDI format, the BeethovANN algorithm, introduced in the last part, needs the information contained in scores in the MusicXML format. These digital scores allow us to formalize relevant musical characteristics in order to generate music with BeethovANN. Thus, to create new voices that can be played in accompaniment to Beethoven string quartets, we take into account not only the note sequences, but also the harmonic progression, interactions between voices, and the rhythm of each instrument. Eventually, professional musicians misclassified BeethovANN musical phrases for Beethovenâs original compositions