We propose a model to represent quasi-flat objects, such as ancient coins. These objects are flat surfaces, meaning their length and their width largely exceed their height, and feature a distinctive relief. This relief characterizes the object and its perception is directly influenced by the position of the object, the light direction and the viewer’s direction. Our model is a single (non classic) image representation containing the underlying structural variations of the object. This model, that we call “Multi-Light Energy Map”, is constructed out of several classic images taken with several illumination directions without computing the object’s surface normals. Together with this information about the magnitude of the object variations, it is possible to compute the information about the angular part, that we call “Multi-Light Orientation Map”. Using these two maps, it is possible to render an image of the object at any light azimuth, enabling the user to move the light sources during the visualization of the object. Moreover, these maps can be useful for the retrieval of ancient coins, either by robust image registration, or by object recognition using either contours or textures.

In this paper a new multichannel object-based audio coding scheme with scalable signal quality is proposed. The novel scheme is based on controlled downmixing and demixing. By means of a dedicated control mechanism, a number of distinct audio objects are mixed into a lower number of channels. The latter is chosen such that the desired quality level is met after demixing. The quality is assessed with two new psychoacoustically motivated metrics. Following the informed source separation approach, the downmix is decomposed via optimum spatial filtering guided by short-time power spectral densities of the audio objects. In an experiment it is shown that the raw data rate of an exemplary 10-track recording can be reduced by at least 30 % using linear pulse-code modulation while maintaining perceptual transparency.

In this work we readdress the issue of audio source separation in an informed scenario, where certain information about the sound sources is embedded into their mixture as an imperceptible watermark. In doing so, we provide a description of an improved algorithm that follows the linearly constrained minimum-variance filtering approach in the subband domain, in order to obtain perceptually better estimates of the source signals in comparison to other published approaches. Just as its predecessor, the algorithm does not impose any restrictions on the number of simultaneously active sources, neither on their spectral overlap. It rather adapts to a given signal constellation and provides the best possible estimates under given constraints in linearithmic time. The validity of the approach is demonstrated on a stereo mixture with two levels of sound complexity. It is also shown by means of both objective and subjective evaluation that the proposed algorithm outperforms a reference algorithm by at least one grade. Bearing high perceptual resemblance to the original signals at a fairly tolerable data rate of 10-20 kbps per source, the algorithm hence seems well-suited for active listening applications such as re-mixing or re-spatialization in real time.

In this paper, the problem of modeling the time-trajectory of the sinusoidal components of voiced speech signals is addressed. A new global approach is presented: a single so-called long-term (LT) model, based on discrete cosine functions, is used to model the overall trajectories of amplitude and phase parameters, for each entire voiced section of speech, differing from usual (short-term) models defined on a frame-by-frame basis. The complete analysis-modeling-synthesis process is presented, including an iterative algorithm for optimal fitting between LT model and measures. A major issue of this paper concerns the use of perceptual criteria in the LT model fitting process (both for amplitude and phase modeling). The adaptation of perceptual criteria usually defined in the short-term and/or stationary cases to the long-term processing is proposed. Experiments dealing with the ten first harmonics of voiced signals show that the proposed approach provides an efficient variable-rate representation of voiced speech signals. Promising results are given in terms of modeling accuracy, synthesis quality, and data compression. The interest of the presented approach for speech coding and speech watermarking is discussed

This paper addresses the problem of tracking partials, i.e., determining the evolution over time of the parameters of a given number of sinusoids with respect to the analyzed audio stream. We first show that the minimal frequency difference heuristic generally used to identify continuities between local maxima of successive short-time spectra can be successfully generalized using the linear prediction formalism to handle modulated sounds such as musical tones with vibrato. The spectral properties of the evolutions in time of the parameters of the partials are next studied to ensure that the parameters of the partials effectively satisfy the slow time-varying constraint of the sinusoidal model. These two improvements are combined in a new algorithm designed for the sinusoidal modeling of polyphonic sounds. The comparative tests show that onsets/offsets of sinusoids as well as closely spaced sinusoids are better identified and stochastic components are better avoided.

The paper proposes to improve further the tracking of partials in a polyphonic context. Spectral characteristics of the controlling parameters (amplitude and frequency) are taken into account to ensure that these parameters evolve slowly with time. The resulting algorithm tracks closely-spaced sinusoids better and is able to avoid most of the spectral data belonging to noise. As a consequence, the proposed algorithm extracts a more meaningful sinusoidal representation from polyphonic recordings.

In this paper, the application of the sinusoidal model for audio/speech signals to the watermarking task is proposed. The basic idea is that adequate modulation of medium rank partials (frequency) trajectories is not perceptible and thus this modulation may contain the data to be embedded in the signal. The modulation (encoding) and estimation (decoding) of the message are described and preliminary promising results are given in the case of speech signals.

In this article, we present an enhanced algorithm, of low complexity, for the tracking of partials in the context of sinusoidal modeling. By considering the past evolution of each partial in the time/frequency and time/amplitude planes to predict its future evolutions, this algorithm allows a better discrimination between sinusoidal and noisy components and an easier cancellation of sudden changes in the evolutions of the partials.

Abstract In this paper, the problem of modeling the trajectory of the phase of speech signal is addressed within the context of the sinusoidal model of speech. A global or long-term model of the trajectory of the phase of the partials is proposed for each entire voiced section of speech, contrary to standard models, which are defined on a frame-by-frame basis. The complete analysis-modeling-synthesis process is presented. We compare two basic long-term models, namely a polynomial and a DCT-based model, with classical (frame-by-frame) interpolation schemes, given that the analysis process is the same in all cases. Promising results are given and the interest of the presented models for speech coding and speech watermarking applications is discussed. 1. Introduction Sinusoidal modeling of audio signals has been extensively studied since the eighties and successfully applied to a wide range of applications, such as coding or time- and frequency-stretching [1-5]. The signal is modeled as the sum of a small number