20 research outputs found
A Cross-Cultural Analysis of Music Structure
PhDMusic signal analysis is a research field concerning the extraction of meaningful information
from musical audio signals. This thesis analyses the music signals from the note-level
to the song-level in a bottom-up manner and situates the research in two Music information
retrieval (MIR) problems: audio onset detection (AOD) and music structural
segmentation (MSS).
Most MIR tools are developed for and evaluated on Western music with specific musical
knowledge encoded. This thesis approaches the investigated tasks from a cross-cultural
perspective by developing audio features and algorithms applicable for both Western and
non-Western genres. Two Chinese Jingju databases are collected to facilitate respectively
the AOD and MSS tasks investigated.
New features and algorithms for AOD are presented relying on fusion techniques. We
show that fusion can significantly improve the performance of the constituent baseline
AOD algorithms. A large-scale parameter analysis is carried out to identify the relations
between system configurations and the musical properties of different music types.
Novel audio features are developed to summarise music timbre, harmony and rhythm for
its structural description. The new features serve as effective alternatives to commonly
used ones, showing comparable performance on existing datasets, and surpass them on
the Jingju dataset. A new segmentation algorithm is presented which effectively captures
the structural characteristics of Jingju. By evaluating the presented audio features and
different segmentation algorithms incorporating different structural principles for the
investigated music types, this thesis also identifies the underlying relations between audio
features, segmentation methods and music genres in the scenario of music structural
analysis.China Scholarship Council
EPSRC C4DM Travel Funding,
EPSRC Fusing Semantic and Audio Technologies for Intelligent Music Production and
Consumption (EP/L019981/1),
EPSRC Platform Grant on Digital Music (EP/K009559/1),
European Research Council project CompMusic, International Society for Music Information Retrieval Student Grant,
QMUL Postgraduate Research Fund,
QMUL-BUPT Joint Programme Funding
Women in Music Information Retrieval Grant
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
A toolkit for live annotation of opera performance: Experiences capturing Wagner's Ring Cycle
[TODO] Add abstract here
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Cultural Context-Aware Models and IT Applications for the Exploitation of Musical Heritage
Information engineering has always expanded its scope by inspiring innovation in different scientific disciplines. In particular, in the last sixty years, music and engineering have forged a strong connection in the discipline known as “Sound and Music Computing”. Musical heritage is a paradigmatic case that includes several multi-faceted cultural artefacts and traditions. Several issues arise from the analog-digital transfer of cultural objects, concerning their creation, preservation, access, analysis and experiencing. The keystone is the relationship of these digitized cultural objects with their carrier and cultural context. The terms “cultural context” and “cultural context awareness” are delineated, alongside the concepts of contextual information and metadata. Since they maintain the integrity of the object, its meaning and cultural context, their role is critical. This thesis explores three main case studies concerning historical audio recordings and ancient musical instruments, aiming to delineate models to preserve, analyze, access and experience the digital versions of these three prominent examples of musical heritage.
The first case study concerns analog magnetic tapes, and, in particular, tape music, a particular experimental music born in the second half of the XX century. This case study has relevant implications from the musicology, philology and archivists’ points of view, since the carrier has a paramount role and the tight connection with its content can easily break during the digitization process or the access phase. With the aim to help musicologists and audio technicians in their work, several tools based on Artificial Intelligence are evaluated in tasks such as the discontinuity detection and equalization recognition. By considering the peculiarities of tape music, the philological problem of stemmatics in digitized audio documents is tackled: an algorithm based on phylogenetic techniques is proposed and assessed, confirming the suitability of these techniques for this task. Then, a methodology for a historically faithful access to digitized tape music recordings is introduced, by considering contextual information and its relationship with the carrier and the replay device. Based on this methodology, an Android app which virtualizes a tape recorder is presented, together with its assessment. Furthermore, two web applications are proposed to faithfully experience digitized 78 rpm discs and magnetic tape recordings, respectively. Finally, a prototype of web application for musicological analysis is presented. This aims to concentrate relevant part of the knowledge acquired in this work into a single interface.
The second case study is a corpus of Arab-Andalusian music, suitable for computational research, which opens new opportunities to musicological studies by applying data-driven analysis. The description of the corpus is based on the five criteria formalized in the CompMusic project of the University Pompeu Fabra of Barcelona: purpose, coverage, completeness, quality and re-usability. Four Jupyter notebooks were developed with the aim to provide a useful tool for computational musicologists for analyzing and using data and metadata of such corpus.
The third case study concerns an exceptional historical musical instrument: an ancient Pan flute exhibited at the Museum of Archaeological Sciences and Art of the University of Padova. The final objective was the creation of a multimedia installation to valorize this precious artifact and to allow visitors to interact with the archaeological find and to learn its history. The case study provided the opportunity to study a methodology suitable for the valorization of this ancient musical instrument, but also extendible to other artifacts or museum collections. Both the methodology and the resulting multimedia installation are presented, followed by the assessment carried out by a multidisciplinary group of experts
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