This work deals with music indexing and alignment using audio codes designed to be representative of the music content and robust to sound modifications. First, based on properties of the Fourier Transform and of the logarithm, highdimensional audio descriptors are designed. Then, a dimension reduction is learned with criteria based on sound discrimination and invariance to transformations. Finally, a binarization is computed to derive codes (integers). This last process allows a fast searching for large catalogs with a hash table, and a Hamming distance on codes makes possible the time alignment using an adapted “Dynamic Time Warping”. The contributions of this paper are tested for two different tasks. The goal of the first task is to identify the segments of music medleys with the audio indexing process, and to accurately find the corresponding original time positions. The goal of the second task is to measure the accuracy of the time-alignment with synthesized MIDI files, where the tempo continuously varies, and with modified pitches and instruments. Additionally, the audio indexing is also tested for these data, in order to exhibit some properties of the used audio prints.

In the tradition of the spectral envelope estimation of periodic sounds, we propose a new accurate method, called True Discrete Cepstrum. Solving a constrained optimization problem, it provides a smooth envelope which fits exactly the given peak values. Moreover, based on the auditory masking, we propose a release of the constraint which improves the smoothness, without perceptual change. Contrarily to some other methods, the parametrization of this method is easy, and it gives a complete control of the maximal deviation of the spectral envelope from the peak values. The benefit of this new method is illustrated and an evaluation procedure validates it with a comparison with some other methods.

Measuring the Room Impulse Responses within a finite 3D spatial domain can require a very large number of measurements with standard uniform sampling. In this paper, we show that, at low frequencies, this sampling can be done with significantly less measurements, using some modal properties of the room. At a given temporal frequency, a plane wave approximation of the acoustic field leads to a sparse approximation, and therefore a compressed sensing framework can be used for its acquisition. This paper describes three different sparse models that can be constructed, and the corresponding estimation algorithms: two models that exploit the structured sparsity of the soundfield, with projections of the modes onto plane waves sharing the same wavenumber, and one that computes a sparse decomposition on a dictionary of independent plane waves with time / space variable separation. These models are compared numerically and experimentally, with an array of 120 microphones irregularly placed within a 2 ×2 ×2 m volume inside a room, with an approximate uniform distribution. One of the most challenging part is the design of estimation algorithms whose computational complexity remains tractable.

This paper deals with the interpolation of the Room Impulse Responses (RIRs) within a whole volume, from as few measurements as possible, and without the knowledge of the geometry of the room. We focus on the early reflections of the RIRs, that have the key property of being sparse in the time domain: this can be exploited in a framework of model-based Compressed Sensing. Starting from a set of RIRs randomly sampled in the spatial domain of interest by a 3D microphone array, we propose a modified Matching Pursuit algorithm to estimate the position of a small set of virtual sources. Then, the reconstruction of the RIRs at interpolated positions is performed using a projection onto a basis of monopoles, which correspond to the estimated virtual sources. An extension of the proposed algorithm allows the interpolation of the positions of both source and receiver, using the acquisition of four different source positions. This approach is validated both by numerical examples, and by experimental measurements using a 3D array with up to 120 microphones.

This paper deals with digital waveguide modeling of wind instruments. It presents the application of state-space representations for the refined acoustic model ofWebster-Lokshin. This acoustic model describes the propagation of longitudinal waves in axisymmetric acoustic pipes with a varying cross-section, visco-thermal losses at the walls, and without assuming planar or spherical waves. Moreover, three types of discontinuities of the shape can be taken into account (radius, slope, and curvature). The purpose of this work is to build low-cost digital simulations in the time domain based on theWebster-Lokshinmodel. First, decomposing a resonator into independent elementary parts and isolating delay operators lead to aKelly-Lochbaumnetwork of input/output systems and delays. Second, for a systematic assembling of elements, their state-space representations are derived in discrete time. Then, standard tools of automatic control are used to reduce the complexity of digital simulations in the time domain. The method is applied to a real trombone, and results of simulations are presented and compared with measurements. This method seems to be a promising approach in term of modularity, complexity of calculation, and accuracy, for any acoustic resonators based on tubes.