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

A joint regression modeling framework for analyzing bivariate binary data in R

We discuss some of the features of the R add-on package GJRM which implements a flexible joint modeling framework for fitting a number of multivariate response regression models under various sampling schemes. In particular,we focus on the case inwhich the user wishes to fit bivariate binary regression models in the presence of several forms of selection bias. The framework allows for Gaussian and non-Gaussian dependencies through the use of copulae, and for the association and mean parameters to depend on flexible functions of covariates. We describe some of the methodological details underpinning the bivariate binary models implemented in the package and illustrate them by fitting interpretable models of different complexity on three data-sets

Measurements of branching fractions, rate asymmetries, and angular distributions in the rare decays B→Kℓ^+ℓ^- and B→K^*ℓ^+ℓ^-

We present measurements of the flavor-changing neutral current decays B→Kℓ^+ℓ^- and B→K^*ℓ^+ℓ^-, where ℓ^+ℓ^- is either an e^+e^- or μ^+μ^- pair. The data sample comprises 229×10^6  Υ(4S)→BB decays collected with the BABAR detector at the PEP-II e^+e^- storage ring. Flavor-changing neutral current decays are highly suppressed in the standard model and their predicted properties could be significantly modified by new physics at the electroweak scale. We measure the branching fractions B(B→Kℓ^+ℓ^-)=(0.34±0.07±0.02)×10^(-6), B(B→K^*ℓ^+ℓ^-)=(0.78-0.17^(+0.19)±0.11)×10^(-6), the direct CP asymmetries of these decays, and the relative abundances of decays to electrons and muons. For two regions in ℓ^+ℓ^- mass, above and below m_(J/ψ), we measure partial branching fractions and the forward-backward angular asymmetry of the lepton pair. In these same regions we also measure the K^* longitudinal polarization in B→K^*ℓ^+ℓ^- decays. Upper limits are obtained for the lepton-flavor-violating decays B→Keμ and B→K^*eμ. All measurements are consistent with standard model expectation

Resonance Production and ππ\pi\pi S-wave in π−+p→π−π−π++precoil\pi^- + p \rightarrow \pi^- \pi^- \pi^+ + p_{recoil} at 190 GeV/c

The COMPASS collaboration has collected the currently largest data set on diffractively produced $\pi^-\pi^-\pi^+$ final states using a negative pion beam of 190 GeV/c momentum impinging on a stationary proton target. This data set allows for a systematic partial-wave analysis in 100 bins of three-pion mass, $0.5 < m_{3\pi} < 2.5$ GeV/c$^2$ , and in 11 bins of the reduced four-momentum transfer squared, $0.1 < t < 1.0$ (GeV/c)$^2$ . This two-dimensional analysis offers sensitivity to genuine one-step resonance production, i.e. the production of a state followed by its decay, as well as to more complex dynamical effects in nonresonant $3\pi$ production. In this paper, we present detailed studies on selected $3\pi$ partial waves with $J^{PC} = 0^{-+}$, $1^{++}$, $2^{-+}$, $2^{++}$, and $4^{++}$. In these waves, we observe the well-known ground-state mesons as well as a new narrow axial-vector meson $a_1(1420)$ decaying into $f_0(980) \pi$. In addition, we present the results of a novel method to extract the amplitude of the $\pi^-\pi^+$ subsystem with $I^{G}J^{PC} = 0^+ 0^{++}$ in various partial waves from the $\pi^-\pi^-\pi^+$ data. Evidence is found for correlation of the $f_0(980)$ and $f_0(1500)$ appearing as intermediate $\pi^- \pi^+$ isobars in the decay of the known $\pi(1800)$ and $\pi_2(1880)$.Comment: 96 page

Improved Subset Autoregression: With R Package

The FitAR R (R Development Core Team 2008) package that is available on the Comprehensive R Archive Network is described. This package provides a comprehensive approach to fitting autoregressive and subset autoregressive time series. For long time series with complicated autocorrelation behavior, such as the monthly sunspot numbers, subset autoregression may prove more feasible and/or parsimonious than using AR or ARMA models. The two principal functions in this package are SelectModel and FitAR for automatic model selection and model fitting respectively. In addition to the regular autoregressive model and the usual subset autoregressive models (Tong'77), these functions implement a new family of models. This new family of subset autoregressive models is obtained by using the partial autocorrelations as parameters and then selecting a subset of these parameters. Further properties and results for these models are discussed in McLeod and Zhang (2006). The advantages of this approach are that not only is an efficient algorithm for exact maximum likelihood implemented but that efficient methods are derived for selecting high-order subset models that may occur in massive datasets containing long time series. A new improved extended {BIC} criterion, {UBIC}, developed by Chen and Chen (2008) is implemented for subset model selection. A complete suite of model building functions for each of the three types of autoregressive models described above are included in the package. The package includes functions for time series plots, diagnostic testing and plotting, bootstrapping, simulation, forecasting, Box-Cox analysis, spectral density estimation and other useful time series procedures. As well as methods for standard generic functions including print, plot, predict and others, some new generic functions and methods are supplied that make it easier to work with the output from FitAR for bootstrapping, simulation, spectral density estimation and Box-Cox analysis.