Radial Basis Function Networks for Conversion of Sound Spectra

In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD) approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency

Accurate glottal model parametrization by integrating audio and high-speed endoscopic video data

The aim of this paper is to evaluate the effectiveness of using video data for voice source parametrization in the representation of voice production through physical modeling. Laryngeal imaging techniques can be effectively used to obtain vocal fold video sequences and to derive time patterns of relevant glottal cues, such as folds edge position or glottal area. In many physically based numerical models of the vocal folds, these parameters are estimated from the inverse filtered glottal flow waveform, obtained from audio recordings of the sound pressure at lips. However, this model inversion process is often problematic and affected by accuracy and robustness issues. It is here discussed how video analysis of the fold vibration might be effectively coupled to the parametric estimation algorithms based on voice recordings, to improve accuracy and robustness of model inversio

Fitting a biomechanical model of the folds to high-speed video data through bayesian estimation

High-speed video recording of the vocal folds during sustained phonation has become a widespread diagnostic tool, and the development of imaging techniques able to perform automated tracking and analysis of relevant glottal cues, such as folds edge position or glottal area, is an active research field. In this paper, a vocal folds vibration analysis method based on the processing of visual data through a biomechanical model of the layngeal dynamics is proposed. The procedure relies on a Bayesian non-stationary estimation of the biomechanical model parameters and state, to fit the folds edge position extracted from the high-speed video endoscopic data. This finely tuned dynamical model is then used as a state transition model in a Bayesian setting, and it allows to obtain a physiologically motivated estimation of upper and lower vocal folds edge position. Based on model prediction, an hypothesis on the lower fold position can be made even in complete fold occlusion conditions occurring during the end of the closed phase and the beginning of the open phase of the glottal cycle. To demonstrate the suitability of the procedure, the method is assessed on a set of audiovisual recordings featuring high-speed video endoscopic data from healthy subjects producing sustained voiced phonation with different laryngeal settings

Incoherent Frequency Fusion for Broadband Steered Response Power Algorithms in Noisy Environments

The steered response power (SRP) algorithms have been shown to be among the most effective and robust ones in noisy environments for direction of arrival (DOA) estimation. In broadband signal applications, the SRP methods typically perform their computations in the frequency-domain by applying a fast Fourier transform (FFT) on a signal portion, calculating the response power on each frequency bin, and subsequently fusing these estimates to obtain the final result. We introduce a frequency response incoherent fusion method based on a normalized arithmetic mean (NAM). Experiments are presented that rely on the SRP algorithms for the localization of motor vehicles in a noisy outdoor environment, focusing our discussion on performance differences with respect to different signal-to-noise ratios (SNR), and on spatial resolution issues for closely spaced sources. We demonstrate that the proposed fusion method provides higher resolution for the delay-and-sum SRP, and improved performances for minimum variance distortionless response (MVDR) and multiple signal classification (MUSIC

Sensitivity-based region selection in the steered response power algorithm

The steered response power (SRP) algorithm is a well-studied method for acoustic source localization using a microphone array. Recently, different improvements based on the accumulation of all time difference of arrival (TDOA) information have been proposed in order to achieve spatial resolution scalability of the grid search map and reduce the computational cost. However, the TDOA information distribution is not uniform with respect to the search grid, as it depends on the geometry of the array, the sampling frequency, and the spatial resolution. In this paper, we propose a sensitivity-based region selection SRP (R-SRP) algorithm that exploits the nonuniform TDOA information accumulation on the search grid. First, high and low sensitivity regions of the search space are identified using an array sensitivity estimation procedure; then, through the formulation of a peak-to-peak ratio (PPR) measuring the peak energy distribution in the two regions, the source is classified to belong to a high or to a low sensitivity region, and this information is used to design an ad hoc weighting function of the acoustic power map on which the grid search is performed. Simulated and real experiments show that the proposed method improves the localization performance in comparison to the state-of-the-art

Exploiting CNNs for Improving Acoustic Source Localization in Noisy and Reverberant Conditions

This paper discusses the application of convolutional neural networks (CNNs) to minimum variance distortionless response localization schemes. We investigate the direction of arrival estimation problems in noisy and reverberant conditions using a uniform linear array (ULA). CNNs are used to process the multichannel data from the ULA and to improve the data fusion scheme, which is performed in the steered response power computation. CNNs improve the incoherent frequency fusion of the narrowband response power by weighting the components, reducing the deleterious effects of those components affected by artifacts due to noise and reverberation. The use of CNNs avoids the necessity of previously encoding the multichannel data into selected acoustic cues with the advantage to exploit its ability in recognizing geometrical pattern similarity. Experiments with both simulated and real acoustic data demonstrate the superior localization performance of the proposed SRP beamformer with respect to other state-of-the-art techniques

Exploiting a geometrically sampled grid in the steered response power algorithm for localization improvement

The steered response power phase transform (SRP-PHAT) is a beamformer method very attractive in acoustic localization applications due to its robustness in reverberant environments. This paper presents a spatial grid design procedure, called the geometrically sampled grid (GSG), which aims at computing the spatial grid by taking into account the discrete sampling of time difference of arrival (TDOA) functions and the desired spatial resolution. A SRP-PHAT localization algorithm based on the GSG method is also introduced. The proposed method exploits the intersections of the discrete hyperboloids representing the TDOA information domain of the sensor array, and projects the whole TDOA information on the space search grid. The GSG method thus allows one to design the sampled spatial grid which represents the best search grid for a given sensor array, it allows one to perform a sensitivity analysis of the array and to characterize its spatial localization accuracy, and it may assist the system designer in the reconfiguration of the array. Experimental results using both simulated data and real recordings show that the localization accuracy is substantially improved both for high and for low spatial resolution, and that it is closely related to the proposed power response sensitivity measure