6 research outputs found
Repertoire-Specific Vocal Pitch Data Generation for Improved Melodic Analysis of Carnatic Music
Deep Learning methods achieve state-of-the-art in many tasks, including vocal pitch extraction. However, these methods rely on the availability of pitch track annotations without errors, which are scarce and expensive to obtain for Carnatic Music. Here we identify the tradition-related challenges and propose tailored solutions to generate a novel, large, and open dataset, the Saraga-Carnatic-Melody-Synth (SCMS), comprising audio mixtures and time-aligned vocal pitch annotations. Through a cross-cultural evaluation leveraging this novel dataset, we show improvements in the performance of Deep Learning vocal pitch extraction methods on Indian Art Music recordings. Additional experiments show that the trained models outperform the currently used heuristic-based pitch extraction solutions for the computational melodic analysis of Carnatic Music and that this improvement leads to better results in the musicologically relevant task of repeated melodic pattern discovery when evaluated using expert annotations. The code and annotations are made available for reproducibility. The novel dataset and trained models are also integrated into the Python package compIAM1 which allows them to be used out-of-the-box
Gesture in Karnatak Music: Pedagogy and Musical Structure in South India
This thesis presents an examination of gesture in Karnatak music, the art music of South India. The topic is approached from two perspectives; the first considers Karnatak music structure from a gestural perspective, looking both at the music itself and at the gestures that create it, while the second enquires into the role played by physical gesture in vocal pedagogy. The broader aims of the thesis are to provide insight into the musical structure of the Karnatak style, and to contribute to wider discourses on connections between music and movement. An interdisciplinary approach to the research is taken, drawing on theories and methods from the fields of ethnomusicology, embodied music cognition, and gesture studies.
The first part of the thesis opens with a discussion of differences between practical and theoretical conceptions of the Karnatak style. I argue for the significance in practice of svara-gamaka units and longer motifs formed of chains of such units, and also consider the gestural qualities of certain motifs and their contribution to bhÄva (mood). Subsequently, I present a joint musical and motoric analysis of a section of Karnatak violin performance, seeking to elucidate the dynamic processes that form the style. The second part of the thesis enquires into the role played by hand gestures produced by teachers and students in vocal lessons, looking at what is indexed by the gestures and how such indexing contributes to the pedagogic process. This part of the thesis also considers how gestures contribute to the formation and maintenance of common ground between teacher and student. The final chapter brings the two strands of this thesis together to discuss the connections that exist between musical and physical gesture in Karnatak music
Motif spotting in an alapana in Carnatic music
ABSTRACT This work addresses the problem of melodic motif spotting, given a query, in Carnatic music. Melody in Carnatic music is based on the concept of raga. Melodic motifs are signature phrases which give a raga its identity. They are also the fundamental units that enable extempore elaborations of a raga. In this paper, an attempt is made to spot typical melodic motifs of a raga queried in a musical piece using a two pass dynamic programming approach, with pitch as the basic feature. In the first pass, the rough longest common subsequence (RLCS) matching is performed between the saddle points of the pitch contours of the reference motif and the musical piece. These saddle points corresponding to quasi-stationary points of the motifs, are relevant entities of the raga. Multiple sequences are identified in this step, not all of which correspond to the the motif that is queried. To reduce the false alarms, in the second pass a fine search using RLCS is performed between the continuous pitch contours of the reference motif and the subsequences obtained in the first pass. The proposed methodology is validated by testing on Alapanas of 20 different musicians
The Music Sound
A guide for music: compositions, events, forms, genres, groups, history, industry, instruments, language, live music, musicians, songs, musicology, techniques, terminology , theory, music video.
Music is a human activity which involves structured and audible sounds, which is used for artistic or aesthetic, entertainment, or ceremonial purposes.
The traditional or classical European aspects of music often listed are those elements given primacy in European-influenced classical music: melody, harmony, rhythm, tone color/timbre, and form. A more comprehensive list is given by stating the aspects of sound: pitch, timbre, loudness, and duration.
Common terms used to discuss particular pieces include melody, which is a succession of notes heard as some sort of unit; chord, which is a simultaneity of notes heard as some sort of unit; chord progression, which is a succession of chords (simultaneity succession); harmony, which is the relationship between two or more pitches; counterpoint, which is the simultaneity and organization of different melodies; and rhythm, which is the organization of the durational aspects of music
Tune your brown clustering, please
Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal