Improved onset detection for traditional flute recordings using convolutional neural networks

The usage of ornaments is key attribute that defines the style of a flute performances within the genre of Irish Traditional Music (ITM). Automated analysis of ornaments in ITM would allow for the musicological investigation of a player’s style and would be a useful feature in the analysis of trends within large corpora of ITM music. As ornament onsets are short and subtle variations within an analysed signal, they are substantially more difficult to detect than longer notes. This paper addresses the topic of onset detection for notes, ornaments and breaths in ITM. We propose a new onset detection method based on a convolutional neural network (CNN) trained solely on flute recordings of ITM. The presented method is evaluated alongside a state-of-the-art gen eralised onset detection method using a corpus of 79 full-length solo flute recordings. The results demonstrate that the proposed system outperforms the generalised system over a range of musi cal patterns idiomatic of the genre

Note, Cut and Strike Detection for Traditional Irish Flute Recordings

This paper addresses the topic of note, cut and strike detection inIrish traditional music (ITM). In order to do this we first evaluate state of the art onset detection methods for identifying note boundaries. Our method utilises the results from manually and automatically segmented flute recordings. We then demonstrate how this information may be utilised for the detection of notes and single note articulations idiomatic of this genre for the purposes of player style identification. Results for manually annotated onsets achieve 86%, 70% and 74% accuracies for note, cut and strike classification respectively. Results for automatically segmented recordings are considerably, lower therefore we perform an analysis of the onset detection results per event class to establish which musical patterns contain the most errors

Wind and Wood : Affordances of Musical Instruments: The Example of the Simple-System Flute

The aim of this doctoral dissertation is to explore and describe the relationship and interaction between musicians and their instruments. In order to achieve a high level of detail, a certain instrument is in focus: the simple-system flute. Although primarily developed as a product of 19th-century Western art music, this instrument has since become established in other genres and traditions.Empirical data is generated through two qualitative studies. Study A consists of interviews with six flute players, including one flute maker. Together they represent a variety of European music traditions, and hence, the simple-system flute is perceived and used in different ways. In the cooperative inquiry of Study B, six flute players came together to investigate their own musical practice and approach towards their instruments.The central analytical concept is affordances, as coined by ecological psychologist James J. Gibson. The concept of affordances is combined with ideas from the emerging research paradigm of 4E cognition, in particular ideas from the extended and enactive dimensions.Through the analysis, affordances of musical instruments are defined as: perceived opportunities for actions arising from the sensorimotor relationship of the interaction with the instrument, as these unfold in the flow of musical practice.The analysis also shows that the cross-modal perceptual experience of the instrument varies between musicians. Viewed through the lens of affordances, this variation entails qualitatively different ways of playing.The perspective on musical learning that emerges through the analysis is discussed in terms of self-organization in which the development of the relationship between musician and instrument allows for an increasing capacity to perceive and act upon affordances of the instrument.This perspective on musical learning implies an understanding of music education as a form of eduction, where the learner is given appropriate space for self-organization and the educator assumes to role of sense-maker of the learning process, and facilitator and moderator of new musical experiences. The dynamic relationship between the individual learner and the educational environment is articulated as an ecological responsibility

Scattering Transform for Playing Technique Recognition

Playing techniques are expressive elements in music performances that carry important information about music expressivity and interpretation. When displaying playing techniques in the time–frequency domain, we observe that each has a distinctive spectro-temporal pattern. Based on the patterns of regularity, we group commonly-used playing techniques into two families: pitch modulation-based techniques (PMTs) and pitch evolution-based techniques (PETs). The former are periodic modulations that elaborate on stable pitches, including vibrato, tremolo, trill, and flutter-tongue; while the latter contain monotonic pitch changes, such as acciaccatura, portamento, and glissando. In this thesis, we present a general framework based on the scattering transform for playing technique recognition. We propose two variants of the scattering transform, the adaptive scattering and the direction-invariant joint scattering. The former provides highly-compact representations that are invariant to pitch transpositions for representing PMTs. The latter captures the spectro-temporal patterns exhibited by PETs. Using the proposed scattering representations as input, our recognition system achieves start-of-the-art results. We provide a formal interpretation of the role of each scattering component confirmed by explanatory visualisations. Whereas previously published datasets for playing technique analysis focused primarily on techniques recorded in isolation, we publicly release a new dataset to evaluate the proposed framework. The dataset, named CBFdataset, is the first dataset on the Chinese bamboo flute (CBF), containing full-length CBF performances and expert annotations of playing techniques. To provide evidence on the generalisability of the proposed framework, we test it over three additional datasets with a variety of playing techniques. Finally, to explore the applicability of the proposed scattering representations to general audio classification problems, we introduce two additional applications: one applies the adaptive scattering for identifying performers in polyphonic orchestral music and the other uses the joint scattering for detecting and classifying chick calls

Computational Modelling and Analysis of Vibrato and Portamento in Expressive Music Performance

PhD, 148ppVibrato and portamento constitute two expressive devices involving continuous pitch modulation and is widely employed in string, voice, wind music instrument performance. Automatic extraction and analysis of such expressive features form some of the most important aspects of music performance research and represents an under-explored area in music information retrieval. This thesis aims to provide computational and scalable solutions for the automatic extraction and analysis of performed vibratos and portamenti. Applications of the technologies include music learning, musicological analysis, music information retrieval (summarisation, similarity assessment), and music expression synthesis. To automatically detect vibratos and estimate their parameters, we propose a novel method based on the Filter Diagonalisation Method (FDM). The FDM remains robust over short time frames, allowing frame sizes to be set at values small enough to accurately identify local vibrato characteristics and pinpoint vibrato boundaries. For the determining of vibrato presence, we test two alternate decision mechanisms—the Decision Tree and Bayes’ Rule. The FDM systems are compared to state-of-the-art techniques and obtains the best results. The FDM’s vibrato rate accuracies are above 92.5%, and the vibrato extent accuracies are about 85%. We use the Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM) to detect portamento existence. Upon extracting the portamenti, we propose a Logistic Model for describing portamento parameters. The Logistic Model has the lowest root mean squared error and the highest adjusted Rsquared value comparing to regression models employing Polynomial and Gaussian functions, and the Fourier Series. The vibrato and portamento detection and analysis methods are implemented in AVA, an interactive tool for automated detection, analysis, and visualisation of vibrato and portamento. Using the system, we perform crosscultural analyses of vibrato and portamento differences between erhu and violin performance styles, and between typical male or female roles in Beijing opera singing

Proceedings of the 18th Irish Conference on Artificial Intelligence and Cognitive Science

These proceedings contain the papers that were accepted for publication at AICS-2007, the 18th Annual Conference on Artificial Intelligence and Cognitive Science, which was held in the Technological University Dublin; Dublin, Ireland; on the 29th to the 31st August 2007. AICS is the annual conference of the Artificial Intelligence Association of Ireland (AIAI)