Xavier Rodet

Time — Domain Formant — Wave — Function Synthesis

Formant-wave-Funct ion (FOF) synthesis is a method for directly calculating the amplitude of the waveform of a signal as a function of time. Many signals can be modeled as a pair: Excitation Function-Parallel Filter. In the FOF method this pair is replaced by a unique formula describing in a more or less approximate way the output of the filter. Two tynes of advantages have motivated the first uses of the FOF technique: on the one hand, in certain cases, the FOF formula can beplified to a point where calculations are fast and easy ([1], [2], [4],[5]). On the other hand, the FOF method allows modeling of signals without the need of separating “a priori” the excitation function and the filter ([3], [53]).

Tracking of partials for additive sound synthesis using hidden Markov models

The authors present a sinusoidal partial tracking method for additive synthesis of sound. Partials are tracked by identifying time functions of parameters as underlying trajectories in a successive set of spectral peaks. This is done by a purely combinatorial hidden Markov model. A partial trajectory is considered as a time sequence of peaks which satisfies continuity constraints on parameter slopes. The method allows frequency line crossing and can be used for formant tracking.<<ETX>>

The CHANT Project: From the Synthesis of the Singing Voice to Synthesis in General

The CHANT project was originally concerned with the analysis and synthesis of the singing voice. This work led to a complex program of voice synthesisby-rule: CHANT. This program was enriched with a constantly expanding software environment, consisting of both analysis and composition programs. In time, broader aims than the synthesis of the singing voice imposed themselves. These aims centered on the search for models of the processes involved in the production of musical sound. Our present research encompasses the physical description of sound phenomena (the sonic material), the articulation of these phenomena (organization), and compositional issues. This research is intended to transcend simulation. Our goal is to extrapolate new creative models for music on the basis of knowledge models developed using the synthesis-by-rule methodology. In this research, synthesis is the proof of both our understanding of sound phenomena, and of the music itself.

Multiple Fundamental Frequency Estimation and Polyphony Inference of Polyphonic Music Signals

This paper presents a frame-based system for estimating multiple fundamental frequencies (F0s) of polyphonic music signals based on the short-time Fourier transform (STFT) representation. To estimate the number of sources along with their F0s, it is proposed to estimate the noise level beforehand and then jointly evaluate all the possible combinations among pre-selected F0 candidates. Given a set of F0 hypotheses, their hypothetical partial sequences are derived, taking into account where partial overlap may occur. A score function is used to select the plausible sets of F0 hypotheses. To infer the best combination, hypothetical sources are progressively combined and iteratively verified. A hypothetical source is considered valid if it either explains more energy than the noise, or improves significantly the envelope smoothness once the overlapping partials are treated. The proposed system has been submitted to Music Information Retrieval Evaluation eXchange (MIREX) 2007 and 2008 contests where the accuracy has been evaluated with respect to the number of sources inferred and the precision of the F0s estimated. The encouraging results demonstrate its competitive performance among the state-of-the-art methods.

Fundamental frequency estimation and tracking using maximum likelihood harmonic matching and HMMs

A new approach is presented for the estimation and tracking of the fundamental frequency (f0) of pseudoperiodic signals. It is based on a probabilistic model of pseudoperiodic signals that makes it possible to take prior knowledge into account and to include constraints on the evolution of the signal. The resulting method can operate on a large interval of f0 values (typically from 50 to 4000 Hz) and on a great variety of sound signals (speech and music signals).<<ETX>>

Multiple fundamental frequency estimation of polyphonic music signals

The article is concerned with the estimation of fundamental frequencies, or F0s, in polyphonic music. We propose a new method for jointly evaluating multiple F0 hypotheses based on three physical principles, harmonicity, spectral smoothness and synchronous amplitude evolution, within a single source. Based on the generative quasiharmonic model, a set of hypothetical partial sequences is derived and an optimal assignment of the observed peaks to the hypothetical sources and noise is performed. The hypothetical partial sequences are then evaluated by a score function which formulates the guiding principles in a mathematical manner. The algorithm has been tested on a large collection of artificially mixed polyphonic samples and the results show the competitive performance of the proposed method.

Estimation of fundamental frequency of musical sound signals

In order to estimate the fundamental frequency (fO) of pseudoperiodical sounds with a wide band of possible fO, a theoretical model based on a maximum likelihood for fO is proposed. The model is simplified to make it fast enough for extensive tests. The resulting algorithm is tested on musical speech sounds. As a musical application, an instrument follower based on the algorithm and operating in real time is implemented.<<ETX>>

Music Transcription with ISA and HMM

We propose a new generative model for polyphonic music based on nonlinear Independent Subspace Analysis (ISA) and factorial Hidden Markov Models (HMM). ISA represents chord spectra as sums of note power spectra and note spectra as sums of instrument-dependent log-power spectra. HMM models note duration. Instrument-dependent parameters are learnt on solo excerpts and used to transcribe musical recordings as collections of notes with time-varying power and other descriptive parameters such as vibrato. We prove the relevance of our modeling assumptions by comparing them with true data distributions and by giving satisfying transcriptions of two duo recordings.

Analysis of sound signals with high resolution matching pursuit

Sound recordings include transients and sustained parts. Their analysis with a basis expansion is not rich enough to represent efficiently all such components. Pursuit algorithms choose the decomposition vectors depending upon the signal properties. The dictionary among which these vectors are selected is much larger than a basis. Matching pursuit is fast to compute, but can provide coarse representations. Basis pursuit gives a better representation but is very expensive in terms of calculation time. This paper develops a high resolution matching pursuit: it is a fast, high time-resolution, time-frequency analysis algorithm, that makes it likely to be used far musical applications.

Phase Minimization for Glottal Model Estimation

In glottal source analysis, the phase minimization criterion has already been proposed to detect excitation instants. As shown in this paper, this criterion can also be used to estimate the shape parameter of a glottal model (ex. Liljencrants-Fant model) and not only its time position. Additionally, we show that the shape parameter can be estimated independently of the glottal model position. The reliability of the proposed methods is evaluated with synthetic signals and compared to that of the IAIF and minimum/maximum-phase decomposition methods. The results of the methods are evaluated according to the influence of the fundamental frequency and noise. The estimation of a glottal model is useful for the separation of the glottal source and the vocal-tract filter and therefore can be applied in voice transformation, synthesis, and also in clinical context or for the study of the voice production.