2,218 research outputs found
Automatic Detection of Melodic Patterns in Flamenco Singing by Analyzing Polyphonic Music Recordings
In this work an analysis of characteristic melodic pattern in flamenco fandango style is carried out. Contrary to
other analysis, where corpora are searched for characteristic melodic patterns, in this work characteristic melodic
patterns are defined by flamenco experts and then searched in the corpora. In our case, the corpora were composed
of pieces taken from two fandango styles, Valverde fandangos and Huelva capital fandangos. The chosen styles are
representative of fandango styles and are also different as for their musical characteristics. The patterns provided by
the flamenco experts were specified in MIDI format, but the corpora under study were provided in audio format.
Two algorithms had to be designed to accomplish the goal of our research: first, an algorithm extracting audio
features from the corpus and outputting a MIDI-like format; second, an algorithm to actually perform the search
based on the output provided by the first algorithm. Flamenco experts assessed the results of the searches and drew
conclusions
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A computational study on outliers in world music
The comparative analysis of world music cultures has been the focus of several ethnomusicological studies in the last century. With the advances of Music Information Retrieval and the increased accessibility of sound archives, large-scale analysis of world music with computational tools is today feasible. We investigate music similarity in a corpus of 8200 recordings of folk and traditional music from 137 countries around the world. In particular, we aim to identify music recordings that are most distinct compared to the rest of our corpus. We refer to these recordings as ‘outliers’. We use signal processing tools to extract music information from audio recordings, data mining to quantify similarity and detect outliers, and spatial statistics to account for geographical correlation. Our findings suggest that Botswana is the country with the most distinct recordings in the corpus and China is the country with the most distinct recordings when considering spatial correlation. Our analysis includes a comparison of musical attributes and styles that contribute to the ‘uniqueness’ of the music of each country
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
From heuristics-based to data-driven audio melody extraction
The identification of the melody from a music recording is a relatively easy task for humans, but very challenging for computational systems. This task is known as "audio melody extraction", more formally defined as the automatic estimation of the pitch sequence of the melody directly from the audio signal of a polyphonic music recording. This thesis investigates the benefits of exploiting knowledge automatically derived from data for audio melody extraction, by combining digital signal processing and machine learning methods. We extend the scope of melody extraction research by working with a varied dataset and multiple definitions of melody. We first present an overview of the state of the art, and perform an evaluation focused on a novel symphonic music dataset. We then propose melody extraction methods based on a source-filter model and pitch contour characterisation and evaluate them on a wide range of music genres. Finally, we explore novel timbre, tonal and spatial features for contour characterisation, and propose a method for estimating multiple melodic lines. The combination of supervised and unsupervised approaches leads to advancements on melody extraction and shows a promising path for future research and applications
16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
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