Estimating the frequency of sinusoidal components is the first part of the sinusoidal analysis chain. Among numerous frequency estimators presented in the literature, we propose to study an estimator proposed in [1] known as the derivative algorithm. Thanks to a trigonometric interpretation of this frequency estimator, we are able to propose a new estimator which improves estimation performance for the frequencies close to the Nyquist frequency without any computational overload.

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Existing deterministic+stochastic spectral models assume that the sounds are with low noise levels. The stochastic part of the sound is generally estimated by subtraction of the deterministic part: It is assumed to be the residual. Inevitable errors in the estimation of the parameters of the deterministic part result in errors – often worse – in the estimation of the stochastic part. We propose a new method that avoids these errors. Our method analyzes the stochastic part without any prior knowledge of the deterministic part. It relies on the study of the distribution of the amplitude values in successive short-time spectra. Computations of the statistical moments or the maximum likelihood lead to an estimation of the noise power density. Experimentations on synthetic or natural sounds show that this method is promising.

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This paper presents a new fast sound synthesis method using polynomials. This is an additive method, where polynomials are used to approximate sine functions. Traditional additive synthesis requires each sample to be generated for each partial oscillator. Then all these partial samples are summed up to obtain the resulting sound sample, thus making the synthesis time proportional to the product of the number of oscillators and the sampling rate. By using polynomial approximations, we instead sum up only the oscillator coefficients and then generate directly the sound sample from these new coefficients. Most of computation time is consumed by a data structure that manages the update of the generator coefficients as a priority queue. Practical implementations show that Polynomial Additive Sound Synthesis (PASS) is particularly efficient for low-frequency signals.

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In this paper we propose a complete computational system for Auditory Scene Analysis. This time-frequency system localizes, separates, and spatializes an arbitrary number of audio sources given only binaural signals. The localization is based on recent research frameworks, where interaural level and time differences are combined to derive a confident direction of arrival (azimuth) at each frequency bin. Here, the power-weighted histogram constructed in the azimuth space is modeled as a Gaussian Mixture Model, whose parameter structure is revealed through a weighted Expectation Maximization. Afterwards, a bank of Gaussian spatial filters is configured automatically to extract the sources with significant energy accordingly to a posterior probability. In this frequency-domain framework, we also inverse a geometrical and physical head model to derive an algorithm that simulates a source as originating from any azimuth angle.

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In this paper, we introduce two original evaluation methods in the context of sinusoidal modeling. The first one assesses the quality of the extraction of sinusoidal components from short-time signals, whereas the second one focuses on the quality of the tracking of these sinusoidal components over time. Each proposed method intends to use a unique cost function that globally reflects the performance of the tested algorithm in a realistic framework. Clearly defined evaluation protocols are then proposed with several test cases to evaluate most of the desired properties of extractors or trackers of sinusoidal components. This paper is a first proposal to be used as a starting point in a sinusoidal analysis / synthesis contest to be held at DAFx’07.

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In the context of non-stationary sinusoidal modeling, this paper introduces the generalization of the derivative method (presented at the first DAFx edition) for the analysis stage. This new method is then compared to the reassignment method for the estimation of all the parameters of the model (phase, amplitude, frequency, amplitude modulation, and frequency modulation), and to the CramérRao bounds. It turns out that the new method is less biased, and thus outperforms the reassignment method in most cases for signalto-noise ratios greater than −10dB.

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We introduce a new technique for the blind localization of several sound sources from two binaural signals. First, the binaural signals are organized as two-dimensional data where each sound source appears as a line. Second, the Hough transform is used to recognize these lines. The slopes of the lines give the mixing coefficients and directions of arrival (azimuths). Two variants of our technique are proposed, based on only one of the interaural level or time differences, respectively. Although a rapid comparison to a well-known localization method as well as promising results are shown, they are clearly not exhaustive and this paper should rather be regarded as a feasibility demonstration of the new technique.

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In this paper is presented the DESAM Toolbox, a set of Matlab functions dedicated to the estimation of widely used spectral models for music signals. Although those models can be used in Music Information Retrieval (MIR) tasks, the core functions of the toolbox do not focus on any specific application. It is rather aimed at providing a range of state-of-the-art signal processing tools that decompose music files according to different signal models, giving rise to different “mid-level” representations. After motivating the need for such a toolbox, this paper offers an overview of the overall organization of the toolbox, and describes all available functionalities.

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Sound applications based on sinusoidal modeling highly depend on the efficiency and the precision of the estimators of its analysis stage. In a previous work, theoretical bounds for the best achievable precision were shown and these bounds are reached by efficient estimators like the reassignment or the derivative methods. We show that it is possible to break these theoretical bounds with just a few additional bits of information of the original content, introducing the concept of “informed analysis”. This paper shows that existing estimators combined with some additional information can reach any expected level of precision, even in very low signal-to-noise ratio conditions, thus enabling high-quality sound effects, without the typical but unwanted musical noise.

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This paper introduces an analysis method based on the generalization of the phase vocoder approach to non-stationary sinusoidal modeling. This new method is then compared to the reassignment method for the estimation of all the parameters of the model (phase, amplitude, frequency, amplitude modulation, and frequency modulation), and to the Cramér-Rao bounds. It turns out that this method compares to the state of the art in terms of performances, with the great advantage of being much simpler.

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