Online Music Style Recognition via Mobile Computing

Music is a widely used data format in the explosion of Internet information. Automatically identifying the style of online music in the Internet is an important and hot topic in the field of music information retrieval and music production. Recently, automatic music style recognition has been used in many real life scenes. Due to the emerging of machine learning, it provides a good foundation for automatic music style recognition. This paper adopts machine learning technology to establish an automatic music style recognition system. First, the online music is process by waveform analysis to remove the noises. Second, the denoised music signals are represented as sample entropy features by using empirical model decomposition. Lastly, the extracted features are used to learn a relative margin support vector machine model to predict future music style. The experimental results demonstrate the effectiveness of the proposed framework.

Sistem Klasifikasi Musik Gamelan Angklung Bali Terhadap Suasana Hati Menggunakan Algoritma K-Nearest Neighbor Berbasis Algoritma Genetika

Musik instrumen gamelan angklung Bali lewat gelombang bunyi yang dihasilkannya mampu menginterferensi gelombang pikiran manusia untuk menurunkan frekuensi gelombang yang dipancarkan oleh otak. Tujuannya untuk mempengaruhi kondisi psikologi yang berkaitan dengan suasana hati agar mengarah pada tingkat stress positif dengan tingkat energi rendah maupun tinggi. Musik dengan tingkat stress positif dan tingkat energi rendah masuk ke dalam kategori suasana hati tenang atau contentment, jika tingkat stress positif dan tingkat energi tinggi masuk ke dalam kategori suasana hati senang atau exuberance. MIR (Music Information Retrieval) adalah bagian dari Data Mining yang menggali informasi mengenai data musik, salah satunya yaitu klasifikasi suasana hati yang diinterpretasikan oleh potongan data musik. Penelitian ini merancang dan membangun sistem klasifikasi untuk mendeteksi suasana hati musik instrumen gamelan angklung Bali menggunakan algoritma K-NN dan K-NN berbasis Algoritma Genetika. K-NN dapat mengatasi masalah klasifikasi dengan baik, namun dibalik keunggulannya, pengaturan nilai k yang sangat sensitif menjadi sebuah kelemahan. Menerapkan operasi genetika oleh Algoritma Genetika pada sistem klasifikasi K-NN berhasil mengoptimasi penentuan nilai k optimal, serta memperbaiki hasil akurasi klasifikasi. Berdasarkan dataset training dan dataset testing yang sama, K-NN memberikan persentase akurasi tertinggi sebesar 81,08% (k=6), sedangkan K-NN berbasis Algoritma Genetika memberikan persentase akurasi tertinggi sebesar 89,19% (k=4).

Musical Information Visualization System

The visualisation systems are spread widely as personal computer’s software. The system, that are processing audio data are presented in this article. The system visualizes the ratio of spectrum amplitudes and has fixed frequency binding to colours. The technology of audio signals processing by the device and components of the device were considered. For the increasing information processing speed was used 32bit controller and graphic equalizer with seven passbands. Music visualization it is function, that are spread widely in mediaplayer’s software, on a different operation systems. This function shows animated images that are depends on music signal. Images are usually reproduced in the real time mode and synchronized with a played audio-track. Music and visualization are merges in the different kind of art: opera, ballett, music drama or movies. Dependencies of auditory and visual sensations are used for increasing the emotional perseption for ordinary listeners . In the systems, that are currently being actively promoted, are used several tools for personal computers, such as: After Effects – The Audio Spectrum Effect, VSDC Video Editor Free – Audio Spectrum Visualizer, Magic Music Visuals. The software, that are mentioned above, has a one disadvantage: the using of streaming video is not possible with the simultaneous receipt of audio and requires processing and rendering of the resulting video series. The purpose of the work is to determine the features of spectral analysis of music information and taking into account real-time data processing. Propose a variant of the music information visualization system, which displays the spectral composition of music and the amplitude of individual harmonics, and filling the LED-matrix with the appropriate color depending on the amplitude of the audio signal, with the possibility of wireless signal transmission from the music source to the visual effects device. The technology of frequency analysis of the spectrum with estimation of amplitude of spectrum’s components of the musical data, that is arriving on the device is chosen for this project. The method is based on the analysis of the spectrum in the selected frequency bands, which in turn simplifies the function of finding maxima at different frequencies. The proposed variant of the musical information visualization system provides display on the LED-matrix of colors that correspond to the frequencies spectrum’s components in the musical composition. Moreover, the number of involved LEDs is proportional to the ratio of the amplitudes of the signal’s frequency components. The desired result is achieved by using a Fast Fourier Transform and selecting Khan or Heming windows for providing a better analysis results of the signal spectrum. The amplitudes of the individual components of the spectrum are estimated additionally and each frequency band has its own color. The work of the system is to analyze the components of the spectrum and frequency of musical information. This information affects the display of colors on the LED matrix. The using of a 32-bit microcontroller provides sufficient speed of audio signal processing with minimal delays. For the increasing the accuracy and speed up the frequency analysis, the sound range is divided into seven bands. For this purpose was used seven-band graphic equalizer MSGEQ7. Music information is transmitted to the system via Bluetooth, which greatly simplifies the selection and connection of the music data source.

Shared and Unshared Feature Extraction in Major Depression During Music Listening Using Constrained Tensor Factorization

Ongoing electroencephalography (EEG) signals are recorded as a mixture of stimulus-elicited EEG, spontaneous EEG and noises, which poses a huge challenge to current data analyzing techniques, especially when different groups of participants are expected to have common or highly correlated brain activities and some individual dynamics. In this study, we proposed a data-driven shared and unshared feature extraction framework based on nonnegative and coupled tensor factorization, which aims to conduct group-level analysis for the EEG signals from major depression disorder (MDD) patients and healthy controls (HC) when freely listening to music. Constrained tensor factorization not only preserves the multilinear structure of the data, but also considers the common and individual components between the data. The proposed framework, combined with music information retrieval, correlation analysis, and hierarchical clustering, facilitated the simultaneous extraction of shared and unshared spatio-temporal-spectral feature patterns between/in MDD and HC groups. Finally, we obtained two shared feature patterns between MDD and HC groups, and obtained totally three individual feature patterns from HC and MDD groups. The results showed that the MDD and HC groups triggered similar brain dynamics when listening to music, but at the same time, MDD patients also brought some changes in brain oscillatory network characteristics along with music perception. These changes may provide some basis for the clinical diagnosis and the treatment of MDD patients.

Enabling FAIR use of Ethnomusicology Data – Through Distributed Repositories, Linked Data and Music Information Retrieval

Recordings of musical practices are kept in various public institutions and private depositories around the world. They constitute valuable data for ethnomusicological research and are substantial for the world's musical heritage. At the moment, there are no commonly used systems and standards for organizing, describing or categorizing these data, which makes their use difficult. In this paper, we discuss the required steps to make them findable, accessible, interoperable and reusable (FAIR), and outline action items to reach these goals. We show solutions that help researchers to manage their data over the whole research lifecycle and discuss the benefits of combining technologies from information science, music information retrieval, and linked data, with the aim of giving incentives for the ethnomusicology research community to actively participate in these developments in the future.

FAIR Interconnection and Enrichment of Public-Domain Music Resources on the Web

Vast amounts of publicly licensed classical music resources are housed within many different repositories on the Web encompassing richly diverse facets of information—including bibliographical and biographical data, digitized images of music notation, music score encodings, audiovisual performance recordings, derived feature data, scholarly commentaries, and listener reactions. While these varied perspectives ought to contribute to greater holistic understanding of the music objects under consideration, in practice, such repositories are typically minimally connected. The TROMPA project aims to improve this situation by interconnecting and enriching public-domain music repositories. This is achieved, on the one hand, by the application of automated, cutting-edge Music Information Retrieval techniques, and on the other, by the development of contribution mechanisms enabling users to integrate their expertise. Information within established repositories is interrelated with data generated by the project within a data infrastructure whose design is guided by the FAIR principles of data management and stewardship: making music information Findable, Accessible, Interoperable, and Reusable. We provide an overview of challenges of description, identification, representation, contribution, and reliability toward applying the FAIR principles to music information, and outline TROMPA's implementational approach to overcoming these challenges. This approach applies a graph-based data infrastructure to interrelate information hosted in different repositories on the Web within a unifying data model (a 'knowledge graph'). Connections are generated across different representations of music content beyond the catalogue level, for instance connecting note elements within score encodings to corresponding moments in performance time-lines. Contributions of user data are supported via privacy-first mechanisms that retain control of such data with the contributing user. Provenance information is captured throughout, supporting reproducibility and re-use of the data both within and outside the context of the project.

Saraga: Open Datasets for Research on Indian Art Music

We introduce two large open data collections of Indian Art Music, both its Carnatic and Hindustani traditions, comprising audio from vocal concerts, editorial metadata, and time-aligned melody, rhythm, and structure annotations. Shared under Creative Commons licenses, they currently form the largest annotated data collections available for computational analysis of Indian Art Music. The collections are intended to provide audio and ground truth for several music information research tasks and large-scale data-driven analysis in musicological studies. A part of the Saraga Carnatic collection also has multitrack recordings, making it a valuable collection for research on melody extraction, source separation, automatic mixing, and performance analysis. We describe the tenets and the process of collection, annotation, and organization of the data. We provide easy access to the audio, metadata, and the annotations in the collections through an API, along with a companion website that has example scripts to facilitate access and use of the data. To sustain and grow the collections, we provide a mechanism for both the research and music community to contribute additional data and annotations to the collections. We also present applications with the collections for music education, understanding, exploration, and discovery.

Audio classification for music information retrieval of Hindustani vocal music

An important task in music information retrieval of Indian art music is the recognition of the larger musicological frameworks, called ragas, on which the performances are based. Ragas are characterized by prominent musical notes, motifs, general sequences of notes used and embellishments improvised by the performers. In this work we propose a convolutional neural network-based model to work on the mel-spectrograms for classication of steady note regions and note transition regions in vocal melodies which can be used for finding prominent musical notes. It is demonstrated that, good classification accuracy is obtained using the proposed model.

Copyright Protection for The Works of Sae Lecturers and Students in The Creative Media Field in The Instant Gratification Era

The protection of ideas or creativity on the work, especially the work of students and lecturers in the creative media field is still not maximized. This can be seen from the rampant piracy of works in the music, information technology, publishing, film, and animation industries. In addition, there are still other problems such as plagiarism of written works, and licensing, especially for the music, photography, and information technology industries. Therefore, it is necessary to have a regulation related to the protection of the works of lecturers and students. The author uses the Normative Juridical research method. The results of the study indicate that the era of instant gratification that was born from technological advances can also have positive and negative impacts, and the protection of the works of lecturers and students who have not been commercialized and are still in the form of drafts that need to be developed further (not yet real) is still not maximized.