Stefania Cecchi

A Mixed Decorrelation Approach for Stereo Acoustic Echo Cancellation Based on the Estimation of the Fundamental Frequency

In teleconferencing systems, undesired echoes due to coupling between the loudspeaker and the microphone can be reduced using acoustic echo cancellers (AECs). In order to introduce better performance in terms of sound localization, at least stereophonic AECs (SAECs) have to be employed. In this case, the correlation between the two channels prevents a correct identification of the echo paths and a signal decorrelation is needed. In this paper, a novel mixed approach to signal decorrelation is proposed based on the estimation of the fundamental frequency. The principle of the proposed structure is to use an approach based on the “missing-fundamental” phenomenon at low frequencies (below 500 Hz), a time-varying all-pass filter controlled by the fundamental frequency at medium and high frequencies, and a time-varying multi-notch filter for improving performance in the upper part of the spectrum. Experiments confirm the effectiveness of the proposed approach in terms of correct identification of the echo paths and stereo perception preservation.

A Combined Psychoacoustic Approach for Stereo Acoustic Echo Cancellation

Acoustic echo cancelers (AECs) are used in teleconferencing systems to reduce undesired echoes originating from coupling between the loudspeaker and the microphone. Better performance in terms of sound localization can be achieved only by using multichannel AECs. By the use of a two-channel (stereo) system, it is already possible to obtain more realistic performance than the monochannel case because listeners have more spatial information that helps to identify the speaker position. In this paper, a novel approach to stereo AEC is proposed based on a combination of psychoacoustic effects. The principle of the proposed structure is to use an approach based on the “missing-fundamental” phenomenon at low frequencies (below 500 Hz) and a modified phase modulation approach at higher frequencies. Several results are presented in terms of magnitude-squared coherence, Itakura-Saito measure, convergence speed of adaptive filters, interaural cross-correlation, and subjective evaluation, in order to confirm the validity of the proposed approach.

Legendre nonlinear filters

The paper discusses a novel sub-class of linear-in-the-parameters nonlinear filters, the Legendre nonlinear filters. The novel sub-class combines the best characteristics of truncated Volterra filters and of the recently introduced even mirror Fourier nonlinear filters, in particular: (i) Legendre nonlinear filters can arbitrarily well approximate any causal, time-invariant, finite-memory, continuous, nonlinear system; (ii) their basis functions are polynomials, specifically, products of Legendre polynomial expansions of the input signal samples; (iii) the basis functions are also mutually orthogonal for white uniform input signals and thus, in adaptive applications, gradient descent algorithms with fast convergence speed can be devised; (iv) perfect periodic sequences can be developed for the identification of Legendre nonlinear filters. A periodic sequence is perfect for a certain nonlinear filter if all cross-correlations between two different basis functions, estimated over a period, are zero. Using perfect periodic sequences as input signals permits the identification of the most relevant basis functions of an unknown nonlinear system by means of the cross-correlation method. Experimental results involving identification of real nonlinear systems illustrate the effectiveness and efficiency of this approach and the potentialities of Legendre nonlinear filters. HighlightsThe paper discusses Legendre nonlinear (LN) filters.LN filters arbitrarily well approximate any finite-memory continuous nonlinear system.The basis functions are mutually orthogonal polynomials for white uniform inputs.Perfect periodic sequences (PPSs) can be developed for their identification.PPSs permit us to identify unknown systems by means of the cross-correlation method.

A novel approach to channel decorrelation for stereo Acoustic Echo Cancellation based on missing fundamental theory

Decorrelation is a well known issue in the context of Stereophonic Acoustic Echo Cancellation: it is related to the problem of uniquely identifying each pair of room acoustic paths, due to high inter-channel coherence. In this paper, a novel approach to decorrelate a stereo signal based on the missing fundamental phenomenon is proposed. An adaptive algorithm is employed to track the behavior of one of the two channels, ensuring a continuous decorrelation without affecting the stereo quality. Several results are presented comparing our approach with Masked Noise injection method in terms of Magnitude Square Coherence, Itakura Saito measure and Convergence Speed of adaptive filters in order to confirm the validity of the proposed approach.

INTRODUCING LEGENDRE NONLINEAR FILTERS

This paper introduces a novel sub-class of linear-in-the-parameters nonlinear filters, the Legendre nonlinear filters. Their basis functions are polynomials, specifically, products of Legendre polynomial expansions of the input signal samples. Legendre nonlinear filters share many of the properties of the recently introduced classes of Fourier nonlinear filters and even mirror Fourier nonlinear filters, which are based on trigonometric basis functions. In fact, Legendre nonlinear filters are universal approximators for causal, time invariant, finite-memory, continuous, nonlinear systems and their basis functions are mutually orthogonal for white uniform input signals. In adaptive applications, gradient descent algorithms with fast convergence speed and efficient nonlinear system identification algorithms can be devised. Experimental results, showing the potentialities of Legendre nonlinear filters in comparison with other linear-in-the-parameters nonlinear filters, are presented and commented.

A New Approach to Bass Enhancement Based on Prony's Method

This paper presents a new practical approach of Pronys Method extended version. The theoretical aspects of the Pronys algorithm are presented and then applied to realize bass improvement of a small loudspeaker through the well known psychoacoustic phenomenon of the missing fundamental. This new method compared to known algorithms based on FFT analysis seems to have best performances and simpler implementation. Objective and subjective evaluations have been performed to validate the virtual bass improvement.

A novel decorrelation approach for multichannel system identification

Multichannel sound reproduction systems aim at providing an optimal acoustical sensation to the listener by enhancing the listening experience and improving the spatial sound impression of the virtual scene. In this context, many audio applications requiring adaptive processing schemes have been developed in order to improve the performance of the audio reproduction. However, online multichannel systems identification requires the introduction of suitable solutions for overcoming the well-known non-uniqueness problem related to the correlation existing among the loudspeaker signals. This issue has been deeply investigated considering stereophonic systems but its extension to multichannel systems may not be so straightforward. In this paper, a novel solution for speech and audio signals is presented based on the “missing-fundamental” phenomenon. Experimental results proved the effectiveness of the approach also making comparisons with the existing state of the art.

Multiple Position Room Response Equalization in Frequency Domain

This paper deals with methods for multiple position room response equalization. Differently from a well-known technique working in the time domain and based on fuzzy c-means clustering, in the proposed approach most of the operations are performed in the frequency domain and, in particular, the fuzzy c-means clustering is applied to the room magnitude responses at different positions. It is shown that working in the frequency domain allows us to obtain equalization performances at least similar to those of the time domain approach with a strongly reduced computational complexity. In addition, different techniques that can replace the fuzzy c-means clustering algorithm in the derivation of the prototype room response equalizer, with additional reduction of the number of operations, are discussed. Finally, the results of three sets of experiments are used to illustrate the performance, the robustness and the quality of the proposed room response equalization method using alternative prototype design strategies applied to different environments.

Identification of Hammerstein model using cubic splines and FIR filtering

Nonlinear models are exploited in the field of digital audio systems for modelling most of real-world devices that show a nonlinear behaviour. Among nonlinear models, Hammerstein systems are realized through a static nonlinearity cascaded with a linear filter. In this paper, the Hammerstein coefficients are estimated using an adaptive Catmull-Rom cubic spline for the static nonlinearity and an adaptive FIR filter for the dynamic linear system also introducing a preprocessing for the time delay estimation. Experimental results confirm the effectiveness of the proposed approach, making also comparisons with existing techniques of the state of the art.

A Combined Approach for Channel Decorrelation in Stereo Acoustic Echo Cancellation Exploiting Time-Varying Frequency Shifting

Multichannel acoustic echo cancellers are used in teleconferencing systems in order to reduce undesired echoes and to introduce better performance in terms of sound localization in the presence of more than one participant. Already stereophonic systems obtain realistic performance, since listeners have spatial information that helps to identify the speaker position. Unfortunately, the correlation between the two channels prevents a correct identification of the echo paths and signal decorrelation is needed. In this letter, a decorrelation approach is proposed, combining the missing-fundamental theory with frequency shifting. Therefore, the proposed approach is based on the computation of an adaptive parameter related to the fundamental frequency that is also used for controlling the desired frequency shift value. Experiments confirm that high performance in terms of magnitude squared coherence reduction and convergence speed increase can be obtained, also comparing it with other approaches of the state of the art and outperforming previous results.