Music Information Research Articles

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

The rapid development of the digital music industry has brought challenges for music lovers and researchers. In response to these challenges, the field of Music Information Retrieval (MIR) emerged in the mid-1960s to capture the complicated and multi-layered nature of music. Among the various approaches explored, machine learning methods have shown promise in overcoming the complexity of this interdisciplinary field. This paper is primarily centered on conducting an in-depth review of the current state of research regarding the utilization of machine learning within the field of Music Information Retrieval (MIR). Additionally, it aims to forecast the potential future directions within the MIR industry. Upon extensive literature review, it becomes evident that various machine learning techniques, including Neural Networks (NN), Support Vector Machines (SVM), and K-nearest neighbors (KNN), have found common applicability in this field. Furthermore, this review highlights Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) as potential algorithms poised to shape the future landscape of MIR. The findings of this paper serve to elucidate the direction in which MIR is progressing, offering valuable guidance for forthcoming research and development endeavors. In doing so, it contributes to the continued progress and maturation of the field MIR.

Edge-cloud computing oriented large-scale online music education mechanism driven by neural networks

With the advent of the big data era, edge cloud computing has developed rapidly. In this era of popular digital music, various technologies have brought great convenience to online music education. But vast databases of digital music prevent educators from making specific-purpose choices. Music recommendation will be a potential development direction for online music education. In this paper, we propose a deep learning model based on multi-source information fusion for music recommendation under the scenario of edge-cloud computing. First, we use the music latent factor vector obtained by the Weighted Matrix Factorization (WMF) algorithm as the ground truth. Second, we build a neural network model to fuse multiple sources of music information, including music spectrum extracted from extra music information to predict the latent spatial features of music. Finally, we predict the user’s preference for music through the inner product of the user vector and the music vector for recommendation. Experimental results on public datasets and real music data collected by edge devices demonstrate the effectiveness of the proposed method in music recommendation.

Short-term retention of popular music in older adults: Support for a plasticity theory of implicit music knowledge acquisition

Based on our Plasticity Theory of Implicit Music Knowledge Acquisition (PTIMKA), we tested the hypothesis that adolescence is a sensitive period for acquiring musical information and lifelong musical grammar. Older adults (N = 27, mean age = 65.7 years; SD = 6.7) identified artist, title, and year of popularity and rated their familiarity for short excerpts of 36 songs popular between 1962 and 2021. Knowledge and familiarity were greater for music popular during the participants’ adolescence. A subsequent surprise retention task required participants to choose which of 2 excerpts had been presented in the first task; for each of 36 trials, targets and foils represented the same era of popularity. Retention (dꞌ) scores and confidence in retention judgment were higher for the songs popular during adolescence, even though targets of all eras had just been presented in the previous 15 minutes. It is argued that popular music styles congruent with an adolescence-established grammar were more accurately encoded than styles violating this grammar, as would songs popular before or after adolescence. Prior data from younger adults showing trajectories opposite to those for older adults as a function of decade of popularity are further consistent with this interpretation and with PTIMKA. (Work supported by NSERC)

The applications of dynamic time warping in the source separation of percussive sounds

Music source separation (MSS) is the process of splitting various components of a musical piece into individual tracks. This process combines the fields of acoustics and machine learning to extract useful data from music, which assists in a variety of music information retrieval tasks. In the past decade, many methods have been employed to perform MSS with varying levels of success. This research explores the use of dynamic time warping (DTW) for MSS tasks in the time domain. DTW is an algorithm that performs a temporal alignment of two time series to measure their similarity. It is unique in that the algorithm will minimize the Euclidean distance between the two sequences by stretching or compressing them to optimize similarity. This makes DTW a distinctive method for MSS, as it operates entirely in the time domain and classifies sounds without the interference of time warping. The research performed focuses only on the separation of transient, percussive sounds. Measurements taken with a drum kit and a selection of digital drum sounds served as the foundation for tests of the algorithm. The results of this research illustrate the potential of DTW in time domain MSS applications.

Classical Turkish Music Composition with LSTM Self-Attention

Synthetic symbolic music generation, the process of creating new musical pieces using symbolic representations, has gained significant traction in the field of music informatics and computational creativity. It holds immense potential for various applications, ranging from music education and composition assistance to music therapy and personalized music recommendation systems. Classical Turkish music (CTM) exhibit distinct characteristics regarding Western Tonal Classical Music (WCM) such as melodic organization, formation of rhythmic structure or melodic expressions. This study tackles the challenge of symbolic music composition, focusing on CTM. Unlike WCM, CTM incorporates microtonal intervals. These intervals are smaller than the semitones in Western music, allowing for a more nuanced expression of pitch. This leads to a more diverse set of pitch ranges. The proposed method employs a combination of self-attention and long-short term memory (LSTM) networks to capture long-term relational information and generate realistic CTM compositions. LSTMs effectively model sequential dependencies and improve local relations within musical structures and self-attention improves the context vector, allowing the model to attend to different aspects of the musical context simultaneously. This combination enables the proposed method to generate compositions that are both musically coherent and stylistically consistent with distinct features of CTM. The proposed method was evaluated on two datasets, the CTM dataset and Classical Music Piano (CPM) dataset. The assessment of musical contents is evaluated through melodic similarity and stylistic consistency metrics. The results demonstrate that the proposed method is able to generate musical content that is coherent and produce music that is pleasing-to-hear. Overall, the article presents a novel and effective approach to symbolic music composition, focusing on CTM.

SOSTAC framework analysis for enhancing digital marketing in Yogyakarta's Art Music Today

This research utilizes a qualitative descriptive methodology, employing the SOSTAC model to analyze Art Music Today (AMT). The study follows six structured steps, starting with a SWOT-based Situation Analysis highlighting AMT's strengths as a unique Yogyakarta entity. Strategic objectives are set using the 5s Framework, focusing on digital marketing elements like online sales optimization and user experience. The Strategy phase emphasizes STP marketing, presenting AMT as a tailored music information center. Tactics, guided by the 7P framework, include product diversification and pricing strategies, contributing to an enhanced user experience. An 18-month action plan, depicted in a Gantt chart, guides implementation from content creation to streamlined processes. The Control phase involves monitoring with Google Analytics and social media evaluation. Through leveraging strengths and addressing weaknesses, AMT positions itself for growth in the digital landscape, using the SOSTAC model as a robust framework for digital marketing excellence. As a suggestion, through a proactive approach to leveraging inherent strengths and addressing weaknesses, AMT aims for significant growth in the dynamic digital environment. Aligning its digital marketing efforts with the SOSTAC model, which covers Situational Analysis, Objectives, Strategy, Tactics, Action, and Control, ensures a comprehensive understanding of current market dynamics. This strategic alignment establishes the foundation for a well-defined and actionable digital marketing strategy.

TenseMusic: An automatic prediction model for musical tension.

The perception of tension and release dynamics constitutes one of the essential aspects of music listening. However, modeling musical tension to predict perception of listeners has been a challenge to researchers. Seminal work demonstrated that tension is reported consistently by listeners and can be accurately predicted from a discrete set of musical features, combining them into a weighted sum of slopes reflecting their combined dynamics over time. However, previous modeling approaches lack an automatic pipeline for feature extraction that would make them widely accessible to researchers in the field. Here, we present TenseMusic: an open-source automatic predictive tension model that operates with a musical audio as the only input. Using state-of-the-art music information retrieval (MIR) methods, it automatically extracts a set of six features (i.e., loudness, pitch height, tonal tension, roughness, tempo, and onset frequency) to use as predictors for musical tension. The algorithm was optimized using Lasso regression to best predict behavioral tension ratings collected on 38 Western classical musical pieces. Its performance was then tested by assessing the correlation between the predicted tension and unseen continuous behavioral tension ratings yielding large mean correlations between ratings and predictions approximating r = .60 across all pieces. We hope that providing the research community with this well-validated open-source tool for predicting musical tension will motivate further work in music cognition and contribute to elucidate the neural and cognitive correlates of tension dynamics for various musical genres and cultures.

Commentary on: "Perception of Structure in Collective Free Improvisation and its Context Dependency" by Arthur Faraco

This short commentary on Arthur Faraco's target paper investigating the perception of structure in collective free improvisation addresses the question of whether the concept of the "interrupt generator" can be used to explain the segmentations annotated by listeners. In addition, the commentary points to opportunities for further modelling of the musical and perceptual data through the use of modern tools from music information retrieval (MIR).

Research on the digital construction mode of college music teaching curriculum based on knowledge mapping

Abstract The study uses the MKR (Multi-task Knowledge-aware Recommendation) model, which integrates knowledge graphs and multilayer perceptron networks, and focuses on mining users’ high-level music preferences from music information and user behaviors. The study first introduces the three core parts of the MKR model: the recommendation task module, the knowledge learning module, and the cross-compression unit. Then, by optimizing the MKR model, the A-MKR (Attention-based MKR) model is proposed, which introduces the attention mechanism to further improve the model performance. In the application of music teaching, the study explores the keyword analysis of music education, digital teaching methods and their effect evaluation. The experimental results show that compared with traditional teaching, the digital teaching model performs better in both field assessment and ability growth, and the error rate is significantly reduced. For example, the digital group scored 1.3 and 9 points higher than the traditional group in field assessment and ability growth, respectively, and the error rate was reduced by 5.8%. This study proves the effectiveness and practical value of knowledge mapping based on the MKR model in the digital transformation of music teaching in colleges and universities. It provides new perspectives and methods for future music teaching.

The Interconnections of Music Structure, Harmony, Melody, Rhythm, and Predictivity

Repetition and structure have a significant place in music theory, but the structure hierarchy and its influences are often ignored in both music analysis and music generation. In this article, we first describe novel algorithms based on repetition to extract music structure hierarchy from a MIDI data set of popular music and show its effectiveness through evaluation. Then, we introduce new data-driven approaches to estimate and validate structural influences in music. Results show that the automatically detected hierarchical repetition structures reveal significant interactions between structure and harmony, melody, rhythm, and predictivity. Different levels of hierarchy interact differently, providing evidence that structural hierarchy plays an important role in our popular music data set beyond simple notions of repetition or similarity. We further study how musical structure has evolved over decades of popular music writing. Finally, we discuss the importance of this work in highlighting roles that structure can play in music analysis, music similarity, music generation, music evaluation, and other music information retrieval tasks.

The Contribution of Music Information Retrieval System Optimization to Music Analysis in the Context of Big Data

The Contribution of Music Information Retrieval System Optimization to Music Analysis in the Context of Big Data

Age- and Sex-Related Differences in Music Requests on a Radio Show for Children

In research on the development of musical preferences, children are often asked either to evaluate musical pieces that have been previously selected by adult experimenters, or to give self-reports on preferences for different musical styles. This study involved analyzing a sample of 1,412 freely and publicly expressed music requests from children aged between 4 and 11 years taken from a German radio program, with regard to age- and sex-specific differences. The music was categorized into genres, as listed on Spotify, and examined using methods of music information retrieval provided by the Spotify Developer application programming interface. Results showed that, at younger ages, the requests were generally more evenly distributed across different genres. Regarding single genres, we observed small positive relationships between age and the likelihood of requesting the genres pop or electro and small negative relationships between age and the likelihood of requesting A capella, German songwriter, indie, theme songs, or children's music. Furthermore, we found that boys requested significantly more rock or hip-hop, whereas girls had a higher tendency to ask for pop. Finally, age- (but no sex-) related differences regarding the Spotify features of valence and liveness of the requested music were found, which were related to the preference for children's music at younger ages. The results thus suggest that previous findings regarding differences between boys and girls and an increasing formation of distinct genre preferences during infancy also apply to single song requests made on a radio show.

THE SYNERGY OF ART AND TECHNICS: DEVELOPMENT DIRECTIONS OF MUSICAL INFORMATION TECHNOLOGIES

This article explores contemporary trends in the development of music information technologies, which refinement constitutes a key factor in the advancement of the music industry. The paper analyzes the advantages and challenges associated with these trends, while also forecasting potential directions for the future development of music information technologies. With an interdisciplinary approach at the intersection of musical art and information technologies, the research highlights the impact of modern technologies, the evolution of microelectronics, computers, and artificial intelligence on music creation and music education. Specifically, the transformation processes of physical sound devices into a virtual dimension are examined. The authors propose a definition of the concept of music information technologies as a symbiosis of information technologies and musical art. In other words, music information technologies are information technologies that address, work on, or influence processes within the field of musical art. The authors conclude that studying the issues related to the functioning of music society created through music information technologies is a promising direction in musicology, music education, and related fields. Based on the analysis of Music Information Technologies trends, it is noted that manufacturers of musical equipment and software developers are gradually transitioning to electronic versions of documentation and video tutorials. Trends include an increase in the number of music file formats, higher sampling frequencies, and the resolution of musical digital systems. The authors highlight the development of specialized digital workstations and other software compatible with general standards but specialized for specific individual physical devices or controllers. The article also anticipates the creation of new methods for controlling musical instruments and devices, widespread use of cloud technologies, and artificial intelligence.

Pattern recognition study of musical rhythm perception based on multimodal deep learning

Abstract Rhythm perception is becoming more and more important in the field of music information processing and music understanding. The study first adopts signal processing methods to extract musical features, then uses feature fusion techniques to integrate features of different modalities into a single feature vector. Based on this model, the study identifies the rhythmic activation function of music and combines it with the hidden Markov model to infer the rhythm of the music. One of the key points of the study is to perform rhythm recognition on music containing drums, to explore the recognition effect. One of the focuses of the study is to recognize the rhythm of music containing drums to explore the recognition effect.In addition, the study also analyzes the Softmax output values of the music and compares the recognition effect of different models. The results show that the rhythm recognition of music using the multimodal deep learning method performs the best in terms of the F-Measure value, the Cemgil value, the Goto value, and the P-score value, with the respective 65.65%, 66.76%, 36.75%, and 36.75%. 66.76%, 36.75%, and 75.68%.Especially in the drum music recognition, the position of each drum music is accurately recognized, proving the model’s effectiveness in this paper. The research provides a new feasible method for the recognition and understanding of music rhythms and a valuable reference for the research in this field.

An Investigation of Chinese College Students' Access to European and American Popular Music in the Social Media Environment

The purpose of this paper is to explore how Chinese college students acquire European and American popular music in the social media environment. With the popularization of the Internet and the development of social media, college students gradually tend to contact and acquire music resources through these emerging platforms. Through questionnaire surveys and case studies, the study found that social media not only became the main channel for college students to obtain information about European and American popular music, but also profoundly influenced their music preferences and choices. Specifically, college students access the latest information, popular singles and album recommendations of European and American pop music through domestic social media platforms such as Weibo, WeChat and Jitterbug, as well as international platforms such as Instagram and YouTube. In addition, they also pay attention to the official accounts of singers, official accounts of music programs, and music recommendation bloggers to get more comprehensive music information. At the same time, the forms of sharing, amenity, and video soundtracks on social media have greatly contributed to the spread of European and American popular music.

Analysis of Audio Differentiation and Sentiment in Korean Songs with Gugak elements : Focusing on Music Information Retrieval and Music Machine Learning

Analysis of Audio Differentiation and Sentiment in Korean Songs with Gugak elements : Focusing on Music Information Retrieval and Music Machine Learning

From Intonation Adjustments to Synchronization of Heart Rate Variability: Singer Interaction in Traditional Georgian Vocal Music

This paper is concerned with how singers of Georgian traditional vocal music interact when singing together. Applying a variety of computational methods from audio signal processing and music information retrieval (MIR), we examine three existing corpora of (field) recordings for manifestations of a high degree of mutual coordination of the singers’ voices. We find numerous examples of harmonically controlled mutual intonation adjustments on both short and long time scales. Furthermore, we believe that the observed differences in melodic and harmonic scales can also be interpreted as (side) effects of the singers’ interaction with the possible goal of achieving harmonic togetherness (or consonance) on the time scale of individual (important) notes. In addition, together with the ensemble Khelkhvavi from Ozurgeti, we conducted an experiment demonstrating the synchronization of singers’ heartbeat rates during the performance of the Gurian song Chven Mshvidoba. The results of our analysis show that a variety of measurable signs of interaction between singers can be observed and documented in existing corpora of Georgian traditional vocal music. Our experience also shows that relevant information about the synchronization of the bodily functions of singers during performances can nowadays be obtained with reasonable technical and logistical effort even in a ‘real world’ framework, thereby allowing us to address questions related to the ‘ecological validity’ of this kind of measurement.

Musical Projects in Guitar Pro 7.5 Sequencer

The purpose of the article is to study the process of creating musical projects in the GUITAR PRO 7.5 sequencer. Research methodology. The article uses a systematic approach, as well as methods of analysis and synthesis, comparison and generalisation. The scientific novelty lies in the identification and development of innovative methods of working in the GUITAR PRO 7.5 sequencer, which allows creating musical compositions from scratch using modern software tools, as well as the possibilities of using music information technologies in the further artistic practice of musicians, sound producers, sound engineers, producers and in professional music education. Conclusions. This software product is a powerful music editor that allows one to create original scores at a professional level for subsequent editing. The programme presents many necessary tools with which the user can work with a variety of musical notation symbols and a wide range of sound dynamics and tempo control, which allows you to create samples of sounding musical pieces and their phonograms. It has a powerful built-in MIDI editor, chord builder, player, metronome, and other useful tools for musicians. Guitar Pro 7.5 can be run on Windows, Linux, and Mac OS platforms. It is widely used by musicians, composers, arrangers, sound engineers, sound designers, and producers. In addition, Guitar Pro 7.5 methods can be used for studying by students who are mastering the relevant specialties in higher educational institutions. Keywords: music notation editor, score, arrangement, sound, musician, composer, sound producer, sound engineer, producer, professional music education.

Music emotion classification using a hybrid CNN-LSTM model

The emotional content of music, interwoven with the intricacies of human affect, poses a unique challenge for computational recognition and classification. With the digitalization of music libraries expanding exponentially, there is a pressing need for precise, automated tools capable of navigating and categorizing vast musical repositories based on emotional contexts. This study advances music emotion classification in the field of music information retrieval by developing a deep learning model that accurately predicts emotional categories in music. The goal of this research is to advance the field of music emotion classification by leveraging the capabilities of convolutional neural networks combined with long short-term memory within deep learning frameworks. The contribution of this study is to provide a refined approach to music emotion classification, combining the power of convolutional neural networks and long short-term memory architectures with sophisticated preprocessing of the Emotify dataset for a deeper and more accurate analysis of musical emotions. The research introduces a novel architecture combining Convolutional Neural Networks and Long Short-Term Memory networks designed to capture the intricate emotional nuances in music. The model leverages convolutional neural networks for robust feature detection and Long Short-Term Memory networks for effective sequence learning, addressing the temporal dynamics of musical features. Utilizing the Emotify dataset, comprising tracks annotated with nine emotional features, the study expands the dataset by segmenting each track into 20 parts, thereby enriching the variety of emotional expressions. Techniques like the synthetic minority oversampling technique were implemented to counter dataset imbalance, ensuring equitable representation of various emotions. The spectral characteristics of the samples were analyzed using the Fast Fourier Transform, contributing to a more comprehensive understanding of the data. Through meticulous fine-tuning, including dropout implementation to prevent overfitting and learning rate adjustments, the developed model achieved a notable accuracy of 94.7 %. This high level of precision underscores the model's potential for application in digital music services, recommendation systems, and music therapy. Future enhancements to this music emotion classification system include expanding the dataset and refining the model architecture for even more nuanced emotional analysis

Cognitive and sensory expectations independently shape musical expectancy and pleasure.

Expectation is crucial for our enjoyment of music, yet the underlying generative mechanisms remain unclear. While sensory models derive predictions based on local acoustic information in the auditory signal, cognitive models assume abstract knowledge of music structure acquired over the long term. To evaluate these two contrasting mechanisms, we compared simulations from four computational models of musical expectancy against subjective expectancy and pleasantness ratings of over 1000 chords sampled from 739 US Billboard pop songs. Bayesian model comparison revealed that listeners' expectancy and pleasantness ratings were predicted by the independent, non-overlapping, contributions of cognitive and sensory expectations. Furthermore, cognitive expectations explained over twice the variance in listeners' perceived surprise compared to sensory expectations, suggesting a larger relative importance of long-term representations of music structure over short-term sensory-acoustic information in musical expectancy. Our results thus emphasize the distinct, albeit complementary, roles of cognitive and sensory expectations in shaping musical pleasure, and suggest that this expectancy-driven mechanism depends on musical information represented at different levels of abstraction along the neural hierarchy. This article is part of the theme issue 'Art, aesthetics and predictive processing: theoretical and empirical perspectives'.