140 research outputs found
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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
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Multiple-instrument polyphonic music transcription using a temporally constrained shift-invariant model
A method for automatic transcription of polyphonic music is proposed in this work that models the temporal evolution of musical tones. The model extends the shift-invariant probabilistic latent component analysis method by supporting the use of spectral templates that correspond to sound states such as attack, sustain, and decay. The order of these templates is controlled using hidden Markov model-based temporal constraints. In addition, the model can exploit multiple templates per pitch and instrument source. The shift-invariant aspect of the model makes it suitable for music signals that exhibit frequency modulations or tuning changes. Pitch-wise hidden Markov models are also utilized in a postprocessing step for note tracking. For training, sound state templates were extracted for various orchestral instruments using isolated note samples. The proposed transcription system was tested on multiple-instrument recordings from various datasets. Experimental results show that the proposed model is superior to a non-temporally constrained model and also outperforms various state-of-the-art transcription systems for the same experiment
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
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