Development of a deep learning system for hummed melody identification for BertsoBot

The system introduced in this work tries to solve the problem of melody classification. The proposed approach is based on extracting the spectrogram of the audio of each melody and then using deep supervised learning approaches to classify them into categories. As found out experimentally, the Transfer Learning technique is required alongside Data Augmentation in order to improve the accuracy of the system. The results shown in this thesis, focus further work on this field by providing insight on the performance of different tested Learning Models. Overall, DenseNets have proved themselves the best architectures o use in this context reaching a significant prediction accuracy

Development of a deep learning system for hummed melody identification for BertsoBot

The system introduced in this work tries to solve the problem of melody classification. The proposed approach is based on extracting the spectrogram of the audio of each melody and then using deep supervised learning approaches to classify them into categories. As found out experimentally, the Transfer Learning technique is required alongside Data Augmentation in order to improve the accuracy of the system. The results shown in this thesis, focus further work on this field by providing insight on the performance of different tested Learning Models. Overall, DenseNets have proved themselves the best architectures o use in this context reaching a significant prediction accuracy

The Role of Cognition in Oral & Written Transmission as Demonstrated in Ritual Chant

This thesis examines the role of cognition in oral and written transmission. It looks at areas of music history where cognition is already used as a reference, including the development of notation, trends and changes in oral transmission, and performance practice. The thesis examines three different case studies on ritual chant in order to demonstrate how the cognitive process can be used to explain the ways learning, retention, and transmission work in oral and written transmission. The first case study is on the chant practices originating in Jerusalem. It discusses the intervallic relationships and music patterns involved in retention of chant, using pitch hierarchy and grouping structure. The second case study is on the Ethiopian Christian chant tradition. It illustrates how shared cognitive processes between oral and written traditions can help explain the ways oral and written traditions work together in preserving ritual. The last case study is on African and Afro-Cuban rituals derived from a common ancestor. It explores sound symbolism and the phonetics of language in chant, and how they work to maintain a stable ritual tradition. The study concludes that cognition plays a greater role in studying oral and written transmission than has been recognized heretofore in historical scholarship

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

Auditory Development in Beginner Elementary Strings Classes

Integrating auditory and visual learning is vital in instrumental music instruction. There is an order of precedence that guides the teaching sequence to raise students who can hear and read the music they play. In order to teach students to think musically while reading notation important auditory preparation needs to take place. It should not occur via passive listening but via active music-making. This practice-based method creates experiential knowledge of music which can then lead to a conceptual understanding of musical symbols. Such practical engagement produces positive long-term effects on the depth of skill and the emotional state of the learner. Despite studies in cognitive development, most method books implement significant reliance on conceptual learning or symbolic representation. Guided by the neuroscience available, this applied research investigates a sound-based approach that serves as a step to traditional method books in elementary string classes. Perspectives on auditory processing and what Csikszentmihalyi refers to as the “flow state” have emerged as exploratory themes among existing literature. Such comprise of personal interviews with participating students who are enrolled in beginning strings classes. To address the gap in research pertaining to learning to read music, this research provides an experience-first curriculum, tracks the engagement, and survey the participating families about their learning experiences. This project will serve as a preparatory method for note-reading and explore the difference in pedagogical sequence between traditional and sound-first methods of teaching beginner strings classes

DMRN+16: Digital Music Research Network One-day Workshop 2021

DMRN+16: Digital Music Research Network One-day Workshop 2021 Queen Mary University of London Tuesday 21st December 2021 Keynote speakers Keynote 1. Prof. Sophie Scott -Director, Institute of Cognitive Neuroscience, UCL. Title: "Sound on the brain - insights from functional neuroimaging and neuroanatomy" Abstract In this talk I will use functional imaging and models of primate neuroanatomy to explore how sound is processed in the human brain. I will demonstrate that sound is represented cortically in different parallel streams. I will expand this to show how this can impact on the concept of auditory perception, which arguably incorporates multiple kinds of distinct perceptual processes. I will address the roles that subcortical processes play in this, and also the contributions from hemispheric asymmetries. Keynote 2: Prof. Gus Xia - Assistant Professor at NYU Shanghai Title: "Learning interpretable music representations: from human stupidity to artificial intelligence" Abstract Gus has been leading the Music X Lab in developing intelligent systems that help people better compose and learn music. In this talk, he will show us the importance of music representation for both humans and machines, and how to learn better music representations via the design of inductive bias. Once we got interpretable music representations, the potential applications are limitless

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