44 research outputs found
Recommended from our members
Bayesian methods in music modelling
This thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is modelled as a set of overlapping sinusoidal basis functions. The parameters of these basis functions are built into a prior framework based on principles known from musical theory and the physics of musical instruments. The model of a musical note optionally includes phenomena such as frequency and amplitude modulations, damping, volume, timbre and inharmonicity. The occurrence of note onsets in a performance of a piece of music is controlled by an underlying tempo process and the alignment of the timings to the underlying score of the music.
A variety of applications are presented for these models under differing inference constraints. Where full Bayesian inference is possible, reversible-jump Markov Chain Monte Carlo is employed to estimate the number of notes and partial frequency components in each frame of music. We also use approximate techniques such as model selection criteria and variational Bayes methods for inference in situations where computation time is limited or the amount of data to be processed is large. For the higher level score parameters, greedy search and conditional modes algorithms are found to be sufficiently accurate.
We emphasize the links between the models and inference algorithms developed in this thesis with that in existing and parallel work, and demonstrate the effects of making modifications to these models both theoretically and by means of experimental results
Automatic transcription of polyphonic music exploiting temporal evolution
PhDAutomatic music transcription is the process of converting an audio recording
into a symbolic representation using musical notation. It has numerous applications
in music information retrieval, computational musicology, and the
creation of interactive systems. Even for expert musicians, transcribing polyphonic
pieces of music is not a trivial task, and while the problem of automatic
pitch estimation for monophonic signals is considered to be solved, the creation
of an automated system able to transcribe polyphonic music without setting
restrictions on the degree of polyphony and the instrument type still remains
open.
In this thesis, research on automatic transcription is performed by explicitly
incorporating information on the temporal evolution of sounds. First efforts address
the problem by focusing on signal processing techniques and by proposing
audio features utilising temporal characteristics. Techniques for note onset and
offset detection are also utilised for improving transcription performance. Subsequent
approaches propose transcription models based on shift-invariant probabilistic
latent component analysis (SI-PLCA), modeling the temporal evolution
of notes in a multiple-instrument case and supporting frequency modulations in
produced notes. Datasets and annotations for transcription research have also
been created during this work. Proposed systems have been privately as well as
publicly evaluated within the Music Information Retrieval Evaluation eXchange
(MIREX) framework. Proposed systems have been shown to outperform several
state-of-the-art transcription approaches.
Developed techniques have also been employed for other tasks related to music
technology, such as for key modulation detection, temperament estimation,
and automatic piano tutoring. Finally, proposed music transcription models
have also been utilized in a wider context, namely for modeling acoustic scenes
Surfing the Waves: Live Audio Mosaicing of an Electric Bass Performance as a Corpus Browsing Interface
In this paper, the authors describe how they use an electric bass as a subtle, expressive and intuitive interface to browse the rich sample bank available to most laptop owners. This is achieved by audio mosaicing of the live bass performance audio, through corpus-based concatenative synthesis (CBCS) techniques, allowing a mapping of the multi-dimensional expressivity of the performance onto foreign audio material, thus recycling the virtuosity acquired on the electric instrument with a trivial learning curve. This design hypothesis is contextualised and assessed within the Sandbox#n series of bass+laptop meta-instruments, and the authors describe technical means of the implementation through the use of the open-source CataRT CBCS system adapted for live mosaicing. They also discuss their encouraging early results and provide a list of further explorations to be made with that rich new interface
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Automatic annotation of musical audio for interactive applications
PhDAs machines become more and more portable, and part of our everyday life, it becomes
apparent that developing interactive and ubiquitous systems is an important
aspect of new music applications created by the research community. We are interested
in developing a robust layer for the automatic annotation of audio signals, to
be used in various applications, from music search engines to interactive installations,
and in various contexts, from embedded devices to audio content servers. We
propose adaptations of existing signal processing techniques to a real time context.
Amongst these annotation techniques, we concentrate on low and mid-level tasks
such as onset detection, pitch tracking, tempo extraction and note modelling. We
present a framework to extract these annotations and evaluate the performances of
different algorithms.
The first task is to detect onsets and offsets in audio streams within short latencies.
The segmentation of audio streams into temporal objects enables various
manipulation and analysis of metrical structure. Evaluation of different algorithms
and their adaptation to real time are described. We then tackle the problem of
fundamental frequency estimation, again trying to reduce both the delay and the
computational cost. Different algorithms are implemented for real time and experimented
on monophonic recordings and complex signals. Spectral analysis can be
used to label the temporal segments; the estimation of higher level descriptions is
approached. Techniques for modelling of note objects and localisation of beats are
implemented and discussed.
Applications of our framework include live and interactive music installations,
and more generally tools for the composers and sound engineers. Speed optimisations
may bring a significant improvement to various automated tasks, such as
automatic classification and recommendation systems. We describe the design of
our software solution, for our research purposes and in view of its integration within
other systems.EU-FP6-IST-507142 project SIMAC (Semantic Interaction with Music
Audio Contents);
EPSRC grants GR/R54620; GR/S75802/01
Automatic transcription of Turkish microtonal music
Automatic music transcription, a central topic in music signal analysis, is typically limited to equal-tempered music and evaluated on a quartertone tolerance level. A system is proposed to automatically transcribe microtonal and heterophonic music as applied to the makam music of Turkey. Specific traits of this music that deviate from properties targeted by current transcription tools are discussed, and a collection of instrumental and vocal recordings is compiled, along with aligned microtonal reference pitch annotations. An existing multi-pitch detection algorithm is adapted for transcribing music with 20 cent resolution, and a method for converting a multi-pitch heterophonic output into a single melodic line is proposed. Evaluation metrics for transcribing microtonal music are applied, which use various levels of tolerance for inaccuracies with respect to frequency and time. Results show that the system is able to transcribe microtonal instrumental music at 20 cent resolution with an F-measure of 56.7%, outperforming state-of-the-art methods for the same task. Case studies on transcribed recordings are provided, to demonstrate the shortcomings and the strengths of the proposed method.QC 20161031</p
Обзор графических вероятностных моделей гармонии для анализа музыкальных произведений
This paper presents the current state of art in the field of automated musical harmony analysis. Research in this field can be motivated by the real-world problems of creating completely automated content-based music recommendation systems (similar to Pandora, but with-out the manual work of expert musicologists). The paper is mainly focused on probabilistic graphical models as one of the most promising approaches, although we also give background in alternative methods. We consider works that use Markov chain models, hidden Markov models, and multi-level graphical models. Along with the models that capture only harmonic information—chord progressions, in some cases also the key,—we also list several models that combine harmonic structure with rhythmic or stream structure.Цель статьи — познакомить читателя с современным состоянием дел в области автоматического анализа музыкальной гармонии. Мотивацией для исследований в этой области может являться создание автоматических систем рекомендации музыки, ориентированных на содержание (наподобие Pandora, но без ручного труда экспертов-музыковедов). Основное внимание уделено графическим вероятностным моделям как одному из наиболее перспективных подходов, но описываются и альтернативные методы. Рассмотрены работы, использующие марковские цепи, скрытые марковские модели, многоуровневые графические модели. Приведены как работы, моделирующие только гармонию — последовательности аккордов, в некоторых случаях и тональность, — так и работы, включающие в себя информацию о структуре анализируемого произведения (ритмической, голосовой)