Acoustic Feature Identification to Recognize Rag Present in Borgit

In the world of Indian classical music, raga recognition is a crucial undertaking. Due to its particular sound qualities, the traditional wind instrument known as the borgit presents special difficulties for automatic raga recognition. In this research, we investigate the use of auditory feature identification methods to create a reliable raga recognition system for Borgit performances. Each of the Borgits, the devotional song of Assam is enriched with rag and each rag has unique melodious tune. This paper has carried out few experiments on the audio samples of rags and a few Borgits sung with those rugs. In this manuscript three mostly used rags and a few Borgits  with these rags are considered for the experiment. Acoustic features considred here are FFT (Fast Fourier Transform), ZCR (Zero Crossing Rates), Mean and Standard deviation of pitch contour and RMS(Root Mean Square). After evaluation and analysis it is seen that FFT  and ZCR are two noteworthy acoustic features that helps to identify the rag present in Borgits. At last K-means clustering was applied on the FFT and ZCR values of the Borgits and were able to find correct grouping according to rags present there. This research validates FFT and ZCR as most precise acoustic parameters for rag identification in Borgit. Here researchers had observed roles of Standard deviation of pitch contour and RMS values of the audio samples in rag identification. &nbsp

Proceedings of the 6th International Workshop on Folk Music Analysis, 15-17 June, 2016

The Folk Music Analysis Workshop brings together computational music analysis and ethnomusicology. Both symbolic and audio representations of music are considered, with a broad range of scientific approaches being applied (signal processing, graph theory, deep learning). The workshop features a range of interesting talks from international researchers in areas such as Indian classical music, Iranian singing, Ottoman-Turkish Makam music scores, Flamenco singing, Irish traditional music, Georgian traditional music and Dutch folk songs. Invited guest speakers were Anja Volk, Utrecht University and Peter Browne, Technological University Dublin

Computational analysis of world music corpora

PhDThe comparison of world music cultures has been considered in musicological research since the end of the 19th century. Traditional methods from the field of comparative musicology typically involve the process of manual music annotation. While this provides expert knowledge, the manual input is timeconsuming and limits the potential for large-scale research. This thesis considers computational methods for the analysis and comparison of world music cultures. In particular, Music Information Retrieval (MIR) tools are developed for processing sound recordings, and data mining methods are considered to study similarity relationships in world music corpora. MIR tools have been widely used for the study of (mainly) Western music. The first part of this thesis focuses on assessing the suitability of audio descriptors for the study of similarity in world music corpora. An evaluation strategy is designed to capture challenges in the automatic processing of world music recordings and different state-of-the-art descriptors are assessed. Following this evaluation, three approaches to audio feature extraction are considered, each addressing a different research question. First, a study of singing style similarity is presented. Singing is one of the most common forms of musical expression and it has played an important role in the oral transmission of world music. Hand-designed pitch descriptors are used to model aspects of the singing voice and clustering methods reveal singing style similarities in world music. Second, a study on music dissimilarity is performed. While musical exchange is evident in the history of world music it might be possible that some music cultures have resisted external musical influence. Low-level audio features are combined with machine learning methods to find music examples that stand out in a world music corpus, and geographical patterns are examined. The last study models music similarity using descriptors learned automatically with deep neural networks. It focuses on identifying music examples that appear to be similar in their audio content but share no (obvious) geographical or cultural links in their metadata. Unexpected similarities modelled in this way uncover possible hidden links between world music cultures. This research investigates whether automatic computational analysis can uncover meaningful similarities between recordings of world music. Applications derive musicological insights from one of the largest world music corpora studied so far. Computational analysis as proposed in this thesis advances the state-of-the-art in the study of world music and expands the knowledge and understanding of musical exchange in the world.Queen Mary Principal’s research studentship

Automatic recognition of Persian musical modes in audio musical signals

This research proposes new approaches for computational identification of Persian musical modes. This involves constructing a database of audio musical files and developing computer algorithms to perform a musical analysis of the samples. Essential features, the spectral average, chroma, and pitch histograms, and the use of symbolic data, are discussed and compared. A tonic detection algorithm is developed to align the feature vectors and to make the mode recognition methods independent of changes in tonality. Subsequently, a geometric distance measure, such as the Manhattan distance, which is preferred, and cross correlation, or a machine learning method (the Gaussian Mixture Models), is used to gauge similarity between a signal and a set of templates that are constructed in the training phase, in which data-driven patterns are made for each dastgàh (Persian mode). The effects of the following parameters are considered and assessed: the amount of training data; the parts of the frequency range to be used for training; down sampling; tone resolution (12-TET, 24-TET, 48-TET and 53-TET); the effect of using overlapping or nonoverlapping frames; and silence and high-energy suppression in pre-processing. The santur (hammered string instrument), which is extensively used in the musical database samples, is described and its physical properties are characterised; the pitch and harmonic deviations characteristic of it are measured; and the inharmonicity factor of the instrument is calculated for the first time. The results are applicable to Persian music and to other closely related musical traditions of the Mediterranean and the Near East. This approach enables content-based analyses of, and content-based searches of, musical archives. Potential applications of this research include: music information retrieval, audio snippet (thumbnailing), music archiving and access to archival content, audio compression and coding, associating of images with audio content, music transcription, music synthesis, music editors, music instruction, automatic music accompaniment, and setting new standards and symbols for musical notation

Musical Beginnings: Musings on Teaching with Music in the Fundamental Design Studio

The beginning design student, like any other. is confronted with a world riddled with a multiplicity of physical, sociological, and psychological conditions that can thwart efforts to objectify contextual design determinants. Given the complexity of the twenty-first century environment, students should perhaps be given a non-building t ype of environment to hone their analytic abilities prior to taking on such a proliferation of perceptual stimuli. Physical environments such as urban settings that typically make up the sites for early design problems are simply too complex for students to first learn to perceive their surroundings in an objective manner. Too many preconceived notions of what constitute such environments cloud their abi lity to analyze such a place. Perhaps an alternative subject exists for students to learn how to discern and organize layers of information into an accurate set of perceptual understandings. While architectural design remains a predominantly visual field of study, our perception of the world involves a myriad of other interconnecting sensory experiences. Tactility and texture can be inferred visually, as can conditions of moistness and dryness. As influential in the design of the built environment as they are, taste, smell, and sound are not easy to graphically communicate. More often than not, the aural experience of the world is left out of the design equation. Unlike the visual arts to which there is little resistance to the use of aural perception in the conception of space, there is can be skepticism regarding attempts to bridge between the aural and visual in architecture

Rojak: a study of cultural elements assimilated in selected works of Malaysian contemporary composers (2001-2014)

Malaysian contemporary music, an emerging and highly diverse art form, has gained recognition both in Malaysia and internationally over the last decade. Nevertheless, few studies have been completed and most conclude that there is no common compositional trend. This study, however, highlights that one common trend has emerged since 2000, namely, Malaysian composers have increasingly assimilated cultural elements into their compositions. The resulting works have been metaphorically compared to the national salad dish, rojak, in which all constituent parts are readily identifiable even when mixed together. It is argued that the assimilation of cultures is central to an understanding of Malaysian contemporary music, even though it is not the only compositional approach. Twenty-nine works by five selected Malaysian composers are analysed in this study. The composers are: Kee Yong Chong (b.1971), Chong Lim Ng (b.1972), Tazul Izan Tajuddin (b.1969), Johan Awang Othman (b.1969), and Kah Hoe Yii (b.1970). Musical score analysis, combined with the interpretation of data collected through fieldwork trips to Malaysia and Singapore, reveal the ways these composers have assimilated a myriad of cultural elements, including gamelan, Malay poem pantun, mak yong [Malaysian ancient theatre], wayang kulit [shadow puppet play], Balinese baris dance, the concept of tenunan [weave] and batik, Chinese calligraphy and painting, Chinese orchestra and its instruments, Chinese philosophy, and Islamic, Buddhist and Christian spiritual practices, into their compositions. This study concludes that their use of idiosyncratic approaches is becoming increasingly distinctive to Malaysian compositions and a reflection of the same processes of mixing identifiable ingredients that is found in the national rojak salads.Thesis (Ph.D.) -- University of Adelaide, Elder Conservatorium of Music, 201

Music similarity analysis using the big data framework spark

A parameterizable recommender system based on the Big Data processing framework Spark is introduced, which takes multiple tonal properties of music into account and is capable of recommending music based on a user's personal preferences. The implemented system is fully scalable; more songs can be added to the dataset, the cluster size can be increased, and the possibility to add different kinds of audio features and more state-of-the-art similarity measurements is given. This thesis also deals with the extraction of the required audio features in parallel on a computer cluster. The extracted features are then processed by the Spark based recommender system, and song recommendations for a dataset consisting of approximately 114000 songs are retrieved in less than 12 seconds on a 16 node Spark cluster, combining eight different audio feature types and similarity measurements.Ein parametrisierbares Empfehlungssystem, basierend auf dem Big Data Framework Spark, wird präsentiert. Dieses berücksichtigt verschiedene klangliche Eigenschaften der Musik und erstellt Musikempfehlungen basierend auf den persönlichen Vorlieben eines Nutzers. Das implementierte Empfehlungssystem ist voll skalierbar. Mehr Lieder können dem Datensatz hinzugefügt werden, mehr Rechner können in das Computercluster eingebunden werden und die Möglichkeit andere Audiofeatures und aktuellere Ähnlichkeitsmaße hizuzufügen und zu verwenden, ist ebenfalls gegeben. Des Weiteren behandelt die Arbeit die parallele Berechnung der benötigten Audiofeatures auf einem Computercluster. Die Features werden von dem auf Spark basierenden Empfehlungssystem verarbeitet und Empfehlungen für einen Datensatz bestehend aus ca. 114000 Liedern können unter Berücksichtigung von acht verschiedenen Arten von Audiofeatures und Abstandsmaßen innerhalb von zwölf Sekunden auf einem Computercluster mit 16 Knoten berechnet werden

Flamenco music information retrieval.

El flamenco, un género musical centrado en la improvisación y la espontaneidad, tiene su origen en el sur de España y atrae a una creciente comunidad de aficionados de países de todo el mundo. El aumento constante y la accesibilidad a colecciones digitales de flamenco, en archivos de música y plataformas online, exige el desarrollo de métodos de análisis y descripción computacionales con el fin de indexar y analizar el contenido musical de manera automática. Music Information Retrieval (MIR) es un área de investigación multidisciplinaria dedicada a la extracción automática de información musical desde grabaciones de audio y partituras. Sin embargo, la gran mayoría de las herramientas existentes se dirigen a la música clásica y la música popular occidental y, a menudo, no se generalizan bien a las tradiciones musicales no occidentales, particularmente cuando las suposiciones relacionadas con la teoría musical no son válidas para estos géneros. Por otro lado, las características y los conceptos musicales específicos de una tradición musical pueden implicar nuevos desafíos computacionales, para los cuales no existen métodos adecuados. Esta tesis enfoca estas limitaciones existentes en el área abordando varios desafíos computacionales que surgen en el contexto de la música flamenca. Con este fin, se realizan una serie de contribuciones en forma de algoritmos novedosos, evaluaciones comparativas y estudios basados en datos, dirigidos a varias dimensiones musicales y que abarcan varias subáreas de ingeniería, matemática computacional, estadística, optimización y musicología computacional. Una particularidad del género, que influye enormemente en el trabajo presentado en esta tesis, es la ausencia de partituras para el cante flamenco. En consecuencia, los métodos computacionales deben basarse únicamente en el análisis de grabaciones, o de transcripciones extraídas automáticamente, lo que genera una colección de nuevos problemas computacionales. Un aspecto clave del flamenco es la presencia de patrones melódicos recurrentes, que esán sujetos a variación y ornamentación durante su interpretación. Desde la perspectiva computacional, identificamos tres tareas relacionadas a esta característica que se abordan en esta tesis: la clasificación por melodía, la búsqueda de secuencias melódicas y la extracción de patrones melódicos. Además, nos acercamos a la tarea de la detección no supervisada de frases melódicas repetidas y exploramos el uso de métodos de deep learning para la identificación de cantaores en grabaciones de video y la segmentación estructural de grabaciones de audio. Finalmente, demostramos en un estudio de minería de datos, cómo una exploración de anotaciones extraídas de manera automática de un corpus amplio de grabaciones nos ayuda a descubrir correlaciones interesantes y asimilar conocimientos sobre este género mayormente indocumentado.Flamenco is a rich performance-oriented art music genre from Southern Spain, which attracts a growing community of aficionados around the globe. The constantly increasing number of digitally available flamenco recordings in music archives, video sharing platforms and online music services calls for the development of genre-specific description and analysis methods, capable of automatically indexing and examining these collections in a content-driven manner. Music Information Retrieval is a multi-disciplinary research area dedicated to the automatic extraction of musical information from audio recordings and scores. Most existing approaches were however developed in the context of popular or classical music and do often not generalise well to non-Western music traditions, in particular when the underlying music theoretical assumptions do not hold for these genres. The specific characteristics and concepts of a music tradition can furthermore imply newcomputational challenges, for which no suitable methods exist. This thesis addresses these current shortcomings of Music Information Retrieval by tackling several computational challenge which arise in the context of flamenco music. To this end, a number of contributions to the field are made in form of novel algorithms, comparative evaluations and data-driven studies, directed at various musical dimensions and encompassing several sub-areas of computer science, computational mathematics, statistics, optimisation and computational musicology. A particularity of flamenco, which immensely shapes the work presented in this thesis, is the absence of written scores. Consequently, computational approaches can solely rely on the direct analysis of raw audio recordings or automatically extracted transcriptions, and this restriction generates set of new computational challenges. A key aspect of flamenco is the presence of reoccurring melodic templates, which are subject to heavy variation during performance. From a computational perspective, we identify three tasks related to this characteristic - melody classification, melody retrieval and melodic template extraction - which are addressed in this thesis. We furthermore approach the task of detecting repeated sung phrases in an unsupervised manner and explore the use of deep learning methods for image-based singer identification in flamenco videos and structural segmentation of flamenco recordings. Finally, we demonstrate in a data-driven corpus study, how automatic annotations can be mined to discover interesting correlations and gain insights into a largely undocumented genre

Redesigning a Performance Practice: Synergising Woodwind Improvisation with Bespoke Software Technology.

This research examines how the designing of a new performance practice based on the incorporation of custom digital signal processing software impacts on solo improvised woodwind performance. Through the development of bespoke software, I investigate how these new technologies can be integrated into solo woodwind performance practice. This research presents a new improvised music practice as well as a suite of new software tools and performance techniques. Through a workshop and performance-­‐based research process, a suite of software processors are developed which respond, and are complementary, to a personalised style of improvised performance. This electronic augmentation of the woodwind instrument (clarinet, bass clarinet, alto saxophone and xaphoon) is tested over the course of thirty solo improvised performances. These performances are documented as audio files and analysed using methods derived from electroacoustic music practice. This research represents an important development in the emerging field of improvised music performance engaging with new digital technologies. The research is practice-­‐led from the viewpoint of an experienced performer and tested in real-­‐world situations, resulting in a useful research outputs embedded in the peer community. Examining the history of live electronic performance practice, this research situates itself within the field of expert performers who use digital processing in free improvisation contexts. A critical understanding of the processes involved allows this researcher to design a new performance practice more effectively. While research necessarily draws on my own performance practice, the knowledge generated will have broad relevance in the field and much of this work is applicable to non-­‐woodwind instrumentalists and singers. The research outputs include freely distributable software created during this project