Virtual 3-D sound can be easily delivered to a listener by binaural audio signals that are reproduced via headphones, which guarantees that only the correct signals reach the corresponding ears. Reproducing the binaural audio signal by two or more loudspeakers introduces the problems of crosstalk on the one hand, and, of reverberation on the other hand. In crosstalk cancellation, the audio signals are fed through a network of prefilters prior to loudspeaker reproduction to ensure that only the designated signal reaches the corresponding ear of the listener. Since room impulse responses are very sensitive to spatial mismatch, and since listeners might slightly move while listening, robust designs are needed. In this paper, we present a method that jointly handles the three problems of crosstalk, reverberation reduction, and spatial robustness with respect to varying listening positions for one or more binaural source signals and multiple listeners. The proposed method is based on a multichannel room impulse response reshaping approach by optimizing a -norm based criterion. Replacing the well-known least-squares technique by a -norm based method employing a large value for allows us to explicitly control the amount of crosstalk and to shape the remaining reverberation effects according to a desired decay.

The purpose of room impulse response (RIR) shortening and reshaping is usually to improve the intelligibility of the received signal by prefiltering the source signal before it is played with a loudspeaker in a closed room. In an alternative, but mathematically equivalent setting, one may aim to postfilter a recorded microphone signal to remove audible echoes. While least-squares methods have mainly been used for the design of shortening/reshaping filters for RIRs until now, we propose to use the infinity- orp-norm as optimization criteria. In our method, design errors will be uniformly distributed over the entire temporal range of the shortened/reshaped global impulse response. In addition, the psychoacoustic property of masking effects is considered during the filter design, which makes it possible to significantly reduce the filter length, compared to standard approaches, without affecting the perceived performance.

The purpose of room impulse response (RIR) shortening is to improve the intelligibility of the received signal by prefiltering the source signal before it is played with a loudspeaker in a closed room. In this paper, we propose to use the infinity-norm as optimization criterion for the design of shortening filters of RIRs. Similar to the equiripple filter design method, design errors will be uniformly distributed over the unwanted temporal range of the shortened global impulse response. The D50 measure is exploited during the design of the shortening filter, which makes it possible to significantly reduce the length of the prefilter without affecting the perceived performance.

This paper presents new results on blind separation of instantaneously mixed independent sources based on high-order statistics together with their time and frequency non-properties (i.e., the non-stationarity and non-whiteness of sources). Separation criteria of mixtures are established on a set of cumulants at different time instants using the non-stationarity of sources and/or time-delayed cumulants using the non-whiteness of sources. It is shown that cumulants at different time instants and time-delayed cumulants can be used as criteria for blind source separation (BSS). Furthermore, it is proved that the cumulant-based separation criteria are directly related to the separability conditions. Batch-data and online learning rules are developed based on the joint diagonalization of symmetric fourth-order cumulant matrices, and the learning rules are further simplified to correlation-based BSS algorithms. In addition, an initialization strategy is proposed for improving the convergence of the learning rules. Simulation results are given to demonstrate the validity and performance of the algorithms.

The concept of disjoint component analysis (DCA) is based on the fact that different speech or audio signals are typically more disjoint than mixtures of them. This letter studies the problem of blind separation of convolutive mixtures through the subband-wise maximization of the disjointness of time-frequency representations of the signals. In our approach, we first define a frequency-dependent measure representing the closeness to disjointness of a group of subband signals. Then, this frequency-dependent measure is integrated to form an objective function that only depends on the time-domain parameters of the separation system. Lastly, an efficient natural-gradient-based learning rule is developed for the update of the separation-system coefficients.

A new technique for the blind separation of convolutive mixtures is proposed in this paper. Inspired by the works of Amari, Sabala , and Rahbar, we firstly start from the application of Kullback-Leibler divergence in frequency domain, and then we integrate Kullback-Leibler divergence over the whole frequency range of interest to yield a new objective function which turns out to be time-domain variable dependent. In other words, the objective function is derived in frequency domain which can be optimized with respect to time domain variables. The proposed technique has the advantages of frequency domain approaches and is suitable for very long mixing channels, but does not suffer from the local permutation problem as the separation is achieved in time-domain