Musical Sequences Research Articles

Stochastic properties of musical time series

Musical sequences are correlated dynamical processes that may differ depending on musical styles. We aim to quantify the correlations through power spectral analysis of pitch sequences in a large corpus of musical compositions as well as improvised performances. Using a multitaper method we extend the power spectral estimates down to the smallest possible frequencies optimizing the tradeoff between bias and variance. The power spectral densities reveal a characteristic behavior; they typically follow inverse power laws (1/f β-noise), yet only down to a cutoff frequency, where they end in a plateau. Correspondingly the pitch autocorrelation function exhibits slow power law decays only up to a cutoff time, beyond which the correlations vanish. We determine cutoff times between 4 and 100 quarter note units for the compositions and improvisations of the corpus, serving as a measure for the degree of persistence and predictability in music. The histogram of exponents β for the power law regimes has a pronounced peak near β = 1 for classical compositions, but is much broader for jazz improvisations.

‘Texting Scarlatti’: unlocking a standard edition with a digital toolkit

Editing musical texts requires a great deal of artistic, intellectual and commercial investment but, once published, an edition tends to become inert, a form of musical still life. ‘Texting Scarlatti’ explores ways of unlocking the value of a standard edition using a variety of digital tools. For as long as the editorial process in music begins and ends with analogue graphical notation, a digital music edition will inevitably be hybrid to some extent. But our project examines each stage in that process from input to output, showing how scholarly insight, performance-based musicology, data science, HCI-inspired crowdsourcing techniques and information systems can together form the basis of an open, rich digital edition for future development. Key outputs will be an extremely large machine-readable dataset consisting of more than 310,000 bars of music, together with structured, searchable apparatus for each of the 555 sonatas in the Scarlatti canon. Musicology has been a notable absentee from recent developments in artificial intelligence and machine learning (AIML) and there is a great deal of catching-up to do. In our conclusion, we urge more scholars and publishers to make similarly large, and largely hidden, datasets more openly available for machine learning and suggest some practical steps to facilitate this.

Generative Adversarial Network Based Music Generation

Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The generator consists of input layer, embedding layer, LSTM layer and generates output with a SoftMax function. Similarly, the discriminator consists of a convolution layer, the output of which is averaged by the global average pooling layer, and output is generated by the sigmoid function. The model is trained on Maestro MIDI dataset. We make the process understandable by delving into the implementation specifics and outlining the fundamental concepts of music. Our effective model highlights the potential of GANs in music composition by producing cohesive music. After training for 50 epochs, the model exhibited a remarkable precision of 91.82 percent. This project uses the combination of Artificial Intelligence (AI) with music theory to provide intriguing new opportunities in the field of music. The model can be beneficial for different industries like gaming, music, entertainment, education, etc.

Acapella-based music generation with sequential models utilizing discrete cosine transform

Making musical instruments that accompany vocals in a song depends on the mood quality and the music composer’s creativity. The model created by other researchers has restrictions that include being limited to musical instrument digital interface files and relying on recurrent neural networks (RNN) or Transformers for the recursive generation of musical notes. This research offers the world’s first model capable of automatically generating musical instruments accompanying human vocal sounds. The model we created is divided into three types of sound input: short input, combed input, and frequency sound based on the discrete cosine transform (DCT). By combining the sequential models such as Autoencoder and gated recurrent unit (GRU) models, we will evaluate the performance of the resulting model in terms of loss and creativity. The best model has a performance evaluation that resulted in an average loss of 0.02993620155. The hearing test results from the sound output produced in the frequency range 0-1,600 Hertz can be heard clearly, and the tones are quite harmonious. The model has the potential to be further developed in future research in the field of sound processing.

Музыка в живописи Натальи Нестеровой

The article discusses the friendship and creative connections between the famous representatives of Russian culture — painter Natalya Nesterova and composer David Krivitsky. The relevance of the topic is connected with Nesterova’s 80th birthday anniversary the world is celebrating. This is a reason to draw attention to the fact that at the moment there is an extremely limited number of scientific works devoted to her heritage. This article for the first time publishes some unknown facts from her artist’s biography and presents her pictures from the composer’s personal archive. The research perspective is to reveal the significance of musical art and performance for N.I. Nesterova and to show the originality of her approach to depicting musicians and the intended symbolic meaning in her paintings with musical subjects. As an exceptional case, the article describes her activities as an illustrator for the music editions of the composer D.I. Krivitsky. It can be concluded that communication between the two bright creative personalities had a mutual influence and served as an impetus for the emergence of interesting works of art.

Age-related neural changes underlying long-term recognition of musical sequences

Aging is often associated with decline in brain processing power and neural predictive capabilities. To challenge this notion, we used magnetoencephalography (MEG) and magnetic resonance imaging (MRI) to record the whole-brain activity of 39 older adults (over 60 years old) and 37 young adults (aged 18–25 years) during recognition of previously memorised and varied musical sequences. Results reveal that when recognising memorised sequences, the brain of older compared to young adults reshapes its functional organisation. In fact, it shows increased early activity in sensory regions such as the left auditory cortex (100 ms and 250 ms after each note), and only moderate decreased activity (350 ms) in medial temporal lobe and prefrontal regions. When processing the varied sequences, older adults show a marked reduction of the fast-scale functionality (250 ms after each note) of higher-order brain regions including hippocampus, ventromedial prefrontal and inferior temporal cortices, while no differences are observed in the auditory cortex. Accordingly, young outperform older adults in the recognition of novel sequences, while no behavioural differences are observed with regards to memorised ones. Our findings show age-related neural changes in predictive and memory processes, integrating existing theories on compensatory neural mechanisms in non-pathological aging.

On the construal of MUSIC IS MOTION metaphor

Abstract TIME IS MOTION can be construed in two models. One is the Ego-based, the other is Time-based so as to form three time metaphors, MOVING TIME, TIME’S LANDSCAPE, and TEMPORAL SEQUENCE. When construing musical motion, people tend to borrow this conceptual framework and get three musical metaphors, i.e. MOVING MUSIC, MUSICAL LANDSCAPE, and MUSICAL SEQUENCE. The MUSICAL SEQUENCE metaphor falls into three categories, i.e. SEQUENCE IS THE RELATIVE POSITION ON THE TIME PATH, ON THE PITCH PATH, and ON THE KEY PATH. Time Metaphor and the Event Structure Metaphor jointly construct the two construal models for the metaphor MUSIC IS MOTION. This study tries to validate the previous research on conceptual construals in such a new field as music so as to enlarge the research range of the conceptual metaphor theory and provide reference for the subsequent research on music.

The Billboard Melodic Music Dataset (BiMMuDa)

We introduce the Billboard Melodic Music Dataset (BiMMuDa), which contains the lead vocal melodies of the top five songs of each year from 1950 to 2022 according to the Billboard year-end singles charts. In this article, the dataset’s compilation process and attributes are described in detail. The melody from each of the 371 songs was transcribed manually in full to create 371 MIDI (musical instrument digital interface) files, and then melodies from the songs’ different sections (e.g., verses, choruses) were exported into separate files to create an additional 1,133 MIDI files of shorter melodies. Lyrics to the songs are provided separately from the melodic transcriptions. This report includes comprehensive descriptions and graphical representations of the available metadata per song and per melody. Analysis of verse and chorus melodies revealed structural differences between them: chorus melodies have significantly fewer notes and lower note density, but larger melodic intervals on average. Whether added to existing datasets or used as a complete dataset, BiMMuDa can serve as ground truth data for a variety of MIR tasks as well as provide insight into the study of Western pop melody.

Spatiotemporal whole-brain activity and functional connectivity of melodies recognition.

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

Sensorimotor Impairment in Aging and Neurocognitive Disorders: Beat Synchronization and Adaptation to Tempo Changes.

Understanding the nature and extent of sensorimotor decline in aging individuals and those with neurocognitive disorders (NCD), such as Alzheimer's disease, is essential for designing effective music-based interventions. Our understanding of rhythmic functions remains incomplete, particularly in how aging and NCD affect sensorimotor synchronization and adaptation to tempo changes. This study aimed to investigate how aging and NCD severity impact tapping to metronomes and music, with and without tempo changes. Patients from a memory clinic participated in a tapping task, synchronizing with metronomic and musical sequences, some of which contained sudden tempo changes. After exclusions, 51 patients were included in the final analysis. Participants' Mini-Mental State Examination scores were associated with tapping consistency. Additionally, age negatively influenced consistency when synchronizing with a musical beat, whereas consistency remained stable across age when tapping with a metronome. The results indicate that the initial decline of attention and working memory with age may impact perception and synchronization to a musical beat, whereas progressive NCD-related cognitive decline results in more widespread sensorimotor decline, affecting tapping irrespective of audio type. These findings underline the importance of customizing rhythm-based interventions to the needs of older adults and individuals with NCD, taking into consideration their cognitive as well as their rhythmic aptitudes.

Part of whose world? How The Little Mermaid (2023) attempts to revise the racist tropes of the 1989 animated film musical

The Little Mermaid (1989) has been the subject of much critical discussion but there has been little attention paid to its representation of race. Utilising textual analysis and drawing upon relevant critical paradigms from Disney studies and scholarship of film musicals, this article argues that The Little Mermaid was an implicitly racist narrative. Through analysis of the musical sequences this article shows how The Little Mermaid created a dichotomy between an exclusively white land life that is coded as superior to multiethnic Mer life. While white society on land is represented as postindustrial, well ordered and crime free, the multiethnic culture under the sea is coded not only as sexually unbridled, where promiscuous fish all “get the urge and start to play” with each other’s bodies, but as a thoroughly corrupt society racked with Welfare Queen coded villainesses dealing in black magic. The article contends that the 2023 remake attempts to revise many of the racist tropes of the original song sequences but, in its depiction of a society in which racial difference is devoid of socio-political significance, The Little Mermaid (2023)—especially in its Berkeley inspired dance sequences—reduces racial difference to a mere aesthetic of colour.

Composing Music for Choreographic Projects: Cases and Trends

All over the world the studies on the collaboration between music composers and dance creators are highly sought after. This is driven by the rapid evolution of musical and choreographic art fields in today’s creative industries. Organizing and promoting such cultural practices is crucial to complement the creative aspect. That’s why the present study examines the tendencies of compositional creativity in Kazakhstan in terms of its integration into choreographic projects. It can also be considered as a projection on cultural management. The article aims to the analytical review of the recent years’ implemented cases, that demonstrate the ways and formats of interaction between music and dance. The article considers the following questions: the interaction of music and choreography in a historical context; the creation of music and compilation of music for ballet; the design of music for dance and dance video, as well as the ways to support compositional creativity.This study of temporal and spatial art forms is built at the interface of several sciences. For the conceptual analysis the authors of the article required a wide range of historical and socio-cultural sources in the specified areas of art. The work was divided into several stages and used analytical, comparative and historical methods projected onto the Kazakh musical theater and other institutions related to the dance art. Also, an interviewing method was applied for the survey of practicing composers and directors, music editors and performers.The design of music and choreography has a wide range of relationships and demands that are necessary for the execution of audiovisual projects.This article with its understanding of cases in the field of ballet theater, dance troupes or video clips may be interesting both theoretically and practically not only for the scholars, but also for composers and choreographers who are only at the beginning of their creative paths. In addition, the article raises the problem of promoting and supporting compositional creativity. A thorough understanding of the music creation process for dance can foster more effective collaborations among creators, as well as the development of cultural management and clusters of creative industries.

MIDI-based generative neural networks with variational autoencoders for innovative music creation

By utilizing variational autoencoder (VAE) architectures in musical instrument digital interface (MIDI)-based generative neural networks (GNNs), this study explores the field of creative music composition. The study evaluates the success of VAEs in generating musical compositions that exhibit both structural integrity and a resemblance to authentic music. Despite achieving convergence in the latent space, the degree of convergence falls slightly short of initial expectations. This prompts an exploration of contributing factors, with a particular focus on the influence of training data variation. The study acknowledges the optimal performance of VAEs when exposed to diverse training data, emphasizing the importance of sufficient intermediate data between extreme ends. The intricacies of latent space dimensions also come under scrutiny, with challenges arising in creating a smaller latent space due to the complexities of representing data in N dimensions. The neural network tends to position data further apart, and incorporating additional information necessitates exponentially more data. Despite the suboptimal parameters employed in the creation and training process, the study concludes that they are sufficient to yield commendable results, showcasing the promising potential of MIDI-based GNNs with VAEs in pushing the boundaries of innovative music composition.

Comparative Symbolic Analysis of the Ethno-Fusion Genre: Insights and Perspectives

Abstract This study explores the integration of music and technology, illustrating their potential to collaboratively push the boundaries of musical exploration. Despite traditionally being viewed as unrelated, the combination of these two fields can significantly contribute to the progress of musical development. This study uses advanced computational methods to build a dataset filled with symbolic musical sequences that belong to a specific genre. This dataset is shown to be highly accurate and provides a detailed analysis of frequencies when examined closely, highlighting its quality and depth. We subject our dataset to comparative analysis with the renowned MAESTRO dataset, employing chromagrams to examine audio signals, rhythms, chords, solos, and note patterns in MIDI format through a variety of methods. This comparison underscores the superior quality of our sequences relative to those in the MAESTRO dataset, emphasizing the meticulousness of our sequence creation process. Moreover, we conduct internal evaluations of our dataset using both three-dimensional and two-dimensional approaches to melody representation, confirming its viability for future scholarly work. This effort seeks to enhance the music field by integrating computer science insights and methodologies, expanding the scope for future music technology research. It highlights the collaborative potential between musical creativity and technological advances in ongoing studies.

Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Our brain is constantly extracting, predicting, and recognising key spatiotemporal features of the physical world in order to survive. While neural processing of visuospatial patterns has been extensively studied, the hierarchical brain mechanisms underlying conscious recognition of auditory sequences and the associated prediction errors remain elusive. Using magnetoencephalography (MEG), we describe the brain functioning of 83 participants during recognition of previously memorised musical sequences and systematic variations. The results show feedforward connections originating from auditory cortices, and extending to the hippocampus, anterior cingulate gyrus, and medial cingulate gyrus. Simultaneously, we observe backward connections operating in the opposite direction. Throughout the sequences, the hippocampus and cingulate gyrus maintain the same hierarchical level, except for the final tone, where the cingulate gyrus assumes the top position within the hierarchy. The evoked responses of memorised sequences and variations engage the same hierarchical brain network but systematically differ in terms of temporal dynamics, strength, and polarity. Furthermore, induced-response analysis shows that alpha and beta power is stronger for the variations, while gamma power is enhanced for the memorised sequences. This study expands on the predictive coding theory by providing quantitative evidence of hierarchical brain mechanisms during conscious memory and predictive processing of auditory sequences.

Enhancing Music Generation With a Semantic-Based Sequence-to-Music Transformer Framework

Music generation became a platform for creative expression, promoting artistic innovation, personalized experiences, and cultural integration, with implications for education and creative industry development. But generating music that resonates emotionally is a challenge. Therefore, we introduce a new framework called the Sequence-to-Music Transformer Framework for Music Generation. This framework employs a simple encoder-decoder Transformer to model music by transforming its fundamental notes into a sequence of discrete tokens. The model learns to generate this sequence token by token. The encoder extracts melodic features of the music, while the decoder uses these extracted features to generate the music sequence. Generation is performed in an auto-regressive manner, meaning the model generates tokens based on previously observed tokens. Music melodic features are integrated into the decoder through cross-attention layers, and the generation process concludes when “end” is generated. The experimental results achieve state-of-the-art performance on a wide range of datasets.

Exploring the Evolution of GenAI in Music Composition

Crafting musical compositions presents a unique set of challenges, divergent from those encountered in visual art forms. The temporal nature of music necessitates models proficient in handling time dynamics. Moreover, compositions often encompass multiple tracks, each characterized by its own temporal intricacies, demanding an intricate approach to their interdependent evolution. Unlike static visual imagery, musical notes are sequenced, commonly organized into chords or melodies, imposing a requirement for specialized chronological structures. This paper extensively delves into the evolutionary journey of GenAI within the domain of sequential generative adversarial networks (GANs) tailored specifically for music composition. We introduce a suite of novel models meticulously crafted to address the nuances of musical generation, exploring their efficacy in producing complex multi-track compositions. Our investigation centers on a comprehensive analysis of the evolutionary trajectory of these models, scrutinizing their capacity to autonomously generate cohesive musical sequences across diverse tracks. Through rigorous empirical evaluations, we substantiate the capabilities of our models to produce compelling musical segments sans human intervention. Furthermore, we delve into intricate technical discussions, elucidating the underlying mechanisms driving the generation process, including the intricate interplay of neural architectures and training methodologies. In addition to empirical validation, we conduct detailed user studies, garnering insights into the subjective perception of the generated compositions. Moreover, we delve into the realm of human-AI collaboration in music generation, unveiling the potential of GenAI to complement human compositions by seamlessly providing harmonious accompaniment, thereby bridging the gap between artistic creativity and computational progress.

Role of the prefrontal cortex in musical and verbal short-term memory: A functional near-infrared spectroscopy study

Abstract Auditory short-term memory (STM) is a key process in auditory cognition, with evidence for partly distinct networks subtending musical and verbal STM. The delayed matching-to-sample task (DMST) paradigm has been found suitable for comparing musical and verbal STM and for manipulating memory load. In this study, musical and verbal DMSTs were investigated with measures of activity in frontal areas with functional near-infrared spectroscopy (fNIRS): Experiment 1 compared musical and verbal DMSTs with a low-level perception task (that does not entail encoding, retention, or retrieval of information), to identify frontal regions involved in memory processes. Experiment 2 manipulated memory load for musical and verbal materials to uncover frontal brain regions showing parametric changes in activity with load and their potential differences between musical and verbal materials. A FIR model was used to deconvolute fNIRS signals across successive trials without making assumptions with respect to the shape of the hemodynamic response in a DMST. Results revealed the involvement of the dorsolateral prefrontal cortex (dlPFC) and inferior frontal gyri (IFG), but not of the superior frontal gyri (SFG) in both experiments, in keeping with previously reported neuroimaging data (including fMRI). Experiment 2 demonstrated a parametric variation of activity with memory load in bilateral IFGs during the maintenance period, with opposite directions for musical and verbal materials. Activity in the IFGs increased with memory load for verbal sound sequences, in keeping with previous results with n-back tasks. The decreased activity with memory load observed with musical sequences is discussed in relation to previous research on auditory STM rehearsal strategies. This study highlights fNIRS as a promising tool for investigating musical and verbal STM not only for typical populations, but also for populations with developmental language disorders associated with functional alterations in auditory STM.

Observable flow experience in Japanese children’s interactions with musical instrument digital interface instruments, violins, and bells

Despite the existing research on flow in music education, there remains a lack of researches in understanding children’s flow experiences in the context of playing musical instrument, electronic technological instruments, and other traditional instruments. This study investigates the observable flow experience among young children during their interactions with musical instrument digital interface instruments, violins, and bell-like instruments. The research employed a case study approach, with 20 children aged two to nine (11 females and nine males) participating in the study. In the study, we, as practitioner-researchers, offered workshops for children to experience a variety of traditional and non-traditional musical instruments. Throughout the study, researchers collected 89 video clips for analysis, using four cameras operated by four assistants who were trained to capture children’s flow in a musical context. These monthly sessions, each lasting an average of 60 min, began with the first session dedicated to capturing the very first encounter of children with various musical devices, instruments, and materials. Custodero’s Flow Indicators in Musical Activities captured children’s flow experiences during workshops. The findings included interpretations of the children’s flow experiences as they explored different devices, instruments, and materials, with a particular emphasis on accessibility and developmental considerations within the social context of children’s musical flow.

Generating Musical Sequences with Transformers

Transformers have significantly revolutionized the music-creation process by their ability to generate intricate and captivating musical arrangements. By analyzing patterns and connections within music data, transformers can produce new compositions with remarkable accuracy and originality. This study explores the internal mechanisms of transformers in music generation and highlights their potential for advancing the field of musical composition. The ability of transformers to capture extensive relationships and contextual information makes them highly suitable for tasks related to music generation. Through self-attention mechanisms, transformers effectively model the dependencies between different time intervals in a musical sequence, resulting in the production of coherent and melodious compositions. This paper delves into the specific architectural elements of transformers that enable them to comprehend and generate musical sequences while also exploring potential applications for transformer-based systems in various creative contexts - emphasizing on significant impact they could have on evolving techniques used during music composition.