We present a probabilistic description of the Harmonic plus Noise Model (HNM) for speech signals. This probabilistic formulation permits Maximum Likelihood (ML) parameter estimation and speech synthesis becomes a straightforward sampling from a distribution. It also permits development of a Kalman filter that tracks model parameters such as pitch, harmonic amplitudes, and autoregressive coeffic...

and Applied Analysis 3 i Preprocessing: it is the first step in training process. It removes the silent part in the front and back of the notes section first and then through a windowed DFT transformation. ii Normalization: signal normalizationmeans converting the signal after preprocessing into equivalent one with mean 0 and variance 1, and it is used to facilitate the follow-up treatment. The...

In this paper we consider the problem of parameter estimation for the multi-component harmonic signals in multiplicative and additive noise. The least squares estimators (LSEs) are proposed for the estimation of the frequencies and the means of random amplitudes, and some statistical results of the LSEs are obtained, including the strong consistency, the strong convergence rate and the asymptot...

We present a novel approach to pitch estimation and note detection in polyphonic audio signals. We pose the problem in a Bayesian probabilistic framework, which allows us to incorporate prior knowledge about the nature of musical data into the model. We exploit the high correlation between model parameters in adjacent frames of data by explicitly modelling the frequency variation over time usin...

The properties and applications of morphological filters for speech analysis are investigated. We introduce and investigate a novel nonlinear spectral envelope estimation method based on morphological operations, which is found to be very robust against noise. This method is also compared with the spectral envelope estimation vocoder (SEEVOC) method. A simple method for the optimum selection of...

Tempo estimation plays a fundamental role in music analysis, especially for the automatic processing of large amounts of musical data. In this paper, a novel idea to enhance the estimation of the tempo in musical pieces is described, based on an harmonic/noise decomposition. This separation of the signal into a deterministic and a stochastic part is performed by projecting the signal onto its n...

We explore the task of optimal quantum channel identification, and in particular the estimation of a general one parameter quantum process. We derive new characterizations of optimality and apply the results to several examples including the qubit depolarizing channel and the harmonic oscillator damping channel. We also discuss the geometry of the problem and illustrate the usefulness of using ...

This paper used in estimation algorithm, for two essential data mining task. i.e classification&regression. it learning to occur the difficult error as octave error,the whole learning process is estimation of perceptual tempo.where the data labels are respect to the propagation on similarity graph constructed based on the feature representation. This paper experiment consider lastFM in 2011.the...

Generally, phonetic classification for low rate speech coding is restricted to either a simple binary voiced/unvoiced classification of entire speech frames, or alternatively, a more complicated estimation of the voicing for a set of frequency bands. A good compromise between these two techniques is estimation of a single cut-off frequency that separates the spectrum into voiced (below) and unv...

We present here an Harmonic+Noise Model (HNM) for speech synthesis. The noise part is represented by an autoregressive model whose output is pitchsynchronously modulated in energy. The harmonic part of the signal is represented by a sinusoidal model. This paper compares di erent methods for separating these two components. We then propose a method for the estimation of the sinusoidal parameters...