Tiemin Mei

Room Impulse Response Shortening/Reshaping With Infinity- and

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- or p-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.

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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.

-Norm Optimization

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

Combined Acoustic MIMO Channel Crosstalk Cancellation and Room Impulse Response Reshaping

In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstationary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms

Blind Source Separation Based on Time-Domain Optimization of a Frequency-Domain Independence Criterion

This paper studies the problem of blind separation of convolutively mixed source signals on the basis of the joint diagonalization (JD) of power spectral density matrices (PSDMs) observed at the output of the separation system. Firstly, a general framework of JD-based blind source separation (BSS) is reviewed and summarized. Special emphasis is put on the separability conditions of sources and mixing system. Secondly, the JD-based BSS is generalized to the separation of convolutive mixtures. The definition of a time and frequency dependent characteristic matrix of sources allows us to state the conditions under which the separation of convolutive mixtures is possible. Lastly, a frequency-domain approach is proposed for convolutive mixture separation. The proposed approach exploits objective functions based on a set of PSDMs. These objective functions are defined in the frequency domain, but are jointly optimized with respect to the time-domain coefficients of the unmixing system. The local permutation ambiguity problems, which are inherent to most frequency-domain approaches, are effectively avoided with the proposed algorithm. Simulation results show that the proposed algorithm is valid for the separation of both simulated and real-word recorded convolutive mixtures.

Blind-Source Separation Based on Decorrelation and Nonstationarity

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.

Blind source separation for convolutive mixtures based on the joint diagonalization of power spectral density matrices

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.

Blind Source Separation Based on Cumulants With Time and Frequency Non-Properties

The purpose of room impulse response (RIR) reshaping or shortening is to accelerate the attenuation of the original RIR so that the reverberation effect will be weakened and the intelligibility of speech played in the associated room will be improved. The unwanted energy of the RIR, which is captured with the help of a window function defined according to the average masking effect of the auditory system, is minimized with the temporal constraint of keeping the infinity norm of the global impulse response constant. Compared with some well known approaches, this new method demonstrates excellent performance in terms of the effectiveness of reshaping/shortening the impulse response while closely retaining the frequency response of the room.

Room impulse response shortening with infinity-norm optimization

Many convolutive blind source separation (BSS) approaches are generalized from instantaneous BSS methods in either time or frequency domain. In this paper, we establish in a general way the inner relationship between the time-domain instantaneous BSS and the frequency-domain convolutive BSS. From this point of view, the time-domain approaches for instantaneous mixture separation are generalized to those for convolutive mixture separation in the frequency domain. Two examples are given to illustrate the feasibility of the proposed approach.

Room impulse response reshaping/shortening based on least mean squares optimizationwith infinity norm constraint

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.