156 research outputs found
Joint Multi-Pitch Detection Using Harmonic Envelope Estimation for Polyphonic Music Transcription
In this paper, a method for automatic transcription of music signals based on joint multiple-F0 estimation is proposed. As a time-frequency representation, the constant-Q resonator time-frequency image is employed, while a novel noise suppression technique based on pink noise assumption is applied in a preprocessing step. In the multiple-F0 estimation stage, the optimal tuning and inharmonicity parameters are computed and a salience function is proposed in order to select pitch candidates. For each pitch candidate combination, an overlapping partial treatment procedure is used, which is based on a novel spectral envelope estimation procedure for the log-frequency domain, in order to compute the harmonic envelope of candidate pitches. In order to select the optimal pitch combination for each time frame, a score function is proposed which combines spectral and temporal characteristics of the candidate pitches and also aims to suppress harmonic errors. For postprocessing, hidden Markov models (HMMs) and conditional random fields (CRFs) trained on MIDI data are employed, in order to boost transcription accuracy. The system was trained on isolated piano sounds from the MAPS database and was tested on classic and jazz recordings from the RWC database, as well as on recordings from a Disklavier piano. A comparison with several state-of-the-art systems is provided using a variety of error metrics, where encouraging results are indicated
Acoustically Inspired Probabilistic Time-domain Music Transcription and Source Separation.
PhD ThesisAutomatic music transcription (AMT) and source separation are important
computational tasks, which can help to understand, analyse and process music
recordings. The main purpose of AMT is to estimate, from an observed
audio recording, a latent symbolic representation of a piece of music (piano-roll).
In this sense, in AMT the duration and location of every note played is
reconstructed from a mixture recording. The related task of source separation
aims to estimate the latent functions or source signals that were mixed
together in an audio recording. This task requires not only the duration and
location of every event present in the mixture, but also the reconstruction
of the waveform of all the individual sounds. Most methods for AMT and
source separation rely on the magnitude of time-frequency representations
of the analysed recording, i.e., spectrograms, and often arbitrarily discard
phase information. On one hand, this decreases the time resolution in AMT.
On the other hand, discarding phase information corrupts the reconstruction
in source separation, because the phase of each source-spectrogram must
be approximated. There is thus a need for models that circumvent phase
approximation, while operating at sample-rate resolution.
This thesis intends to solve AMT and source separation together from
an unified perspective. For this purpose, Bayesian non-parametric signal
processing, covariance kernels designed for audio, and scalable variational
inference are integrated to form efficient and acoustically-inspired probabilistic
models. To circumvent phase approximation while keeping sample-rate
resolution, AMT and source separation are addressed from a Bayesian time-domain
viewpoint. That is, the posterior distribution over the waveform of
each sound event in the mixture is computed directly from the observed data.
For this purpose, Gaussian processes (GPs) are used to define priors over the
sources/pitches. GPs are probability distributions over functions, and its
kernel or covariance determines the properties of the functions sampled from
a GP. Finally, the GP priors and the available data (mixture recording) are
combined using Bayes' theorem in order to compute the posterior distributions
over the sources/pitches.
Although the proposed paradigm is elegant, it introduces two main challenges.
First, as mentioned before, the kernel of the GP priors determines the
properties of each source/pitch function, that is, its smoothness, stationariness,
and more importantly its spectrum. Consequently, the proposed model
requires the design of flexible kernels, able to learn the rich frequency content
and intricate properties of audio sources. To this end, spectral mixture
(SM) kernels are studied, and the Mat ern spectral mixture (MSM) kernel
is introduced, i.e. a modified version of the SM covariance function. The
MSM kernel introduces less strong smoothness, thus it is more suitable for
modelling physical processes. Second, the computational complexity of GP
inference scales cubically with the number of audio samples. Therefore, the
application of GP models to large audio signals becomes intractable. To
overcome this limitation, variational inference is used to make the proposed
model scalable and suitable for signals in the order of hundreds of thousands
of data points.
The integration of GP priors, kernels intended for audio, and variational
inference could enable AMT and source separation time-domain methods to
reconstruct sources and transcribe music in an efficient and informed manner.
In addition, AMT and source separation are current challenges, because
the spectra of the sources/pitches overlap with each other in intricate
ways. Thus, the development of probabilistic models capable of differentiating
sources/pitches in the time domain, despite the high similarity between
their spectra, opens the possibility to take a step towards solving source separation
and automatic music transcription. We demonstrate the utility of our
methods using real and synthesized music audio datasets for various types of
musical instruments
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Signal separation of musical instruments: simulation-based methods for musical signal decomposition and transcription
This thesis presents techniques for the modelling of musical signals, with particular regard to monophonic and polyphonic pitch estimation. Musical signals are modelled as a set of notes, each comprising of a set of harmonically-related sinusoids. An hierarchical model is presented that is very general and applicable to any signal that can be decomposed as the sum of basis functions. Parameter estimation is posed within a Bayesian framework, allowing for the incorporation of prior information about model parameters. The resulting posterior distribution is of variable dimension and so reversible jump MCMC simulation techniques are employed for the parameter estimation task. The extension of the model to time-varying signals with high posterior correlations between model parameters is described. The parameters and hyperparameters of several frames of data are estimated jointly to achieve a more robust detection. A general model for the description of time-varying homogeneous and heterogeneous multiple component signals is developed, and then applied to the analysis of musical signals. The importance of high level musical and perceptual psychological knowledge in the formulation of the model is highlighted, and attention is drawn to the limitation of pure signal processing techniques for dealing with musical signals. Gestalt psychological grouping principles motivate the hierarchical signal model, and component identifiability is considered in terms of perceptual streaming where each component establishes its own context. A major emphasis of this thesis is the practical application of MCMC techniques, which are generally deemed to be too slow for many applications. Through the design of efficient transition kernels highly optimised for harmonic models, and by careful choice of assumptions and approximations, implementations approaching the order of realtime are viable.Engineering and Physical Sciences Research Counci
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
An End-to-End Neural Network for Polyphonic Piano Music Transcription
We present a supervised neural network model for polyphonic piano music
transcription. The architecture of the proposed model is analogous to speech
recognition systems and comprises an acoustic model and a music language model.
The acoustic model is a neural network used for estimating the probabilities of
pitches in a frame of audio. The language model is a recurrent neural network
that models the correlations between pitch combinations over time. The proposed
model is general and can be used to transcribe polyphonic music without
imposing any constraints on the polyphony. The acoustic and language model
predictions are combined using a probabilistic graphical model. Inference over
the output variables is performed using the beam search algorithm. We perform
two sets of experiments. We investigate various neural network architectures
for the acoustic models and also investigate the effect of combining acoustic
and music language model predictions using the proposed architecture. We
compare performance of the neural network based acoustic models with two
popular unsupervised acoustic models. Results show that convolutional neural
network acoustic models yields the best performance across all evaluation
metrics. We also observe improved performance with the application of the music
language models. Finally, we present an efficient variant of beam search that
improves performance and reduces run-times by an order of magnitude, making the
model suitable for real-time applications
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