This paper proposes a method for online estimation of time-varying room impulse responses (RIR) between multiple isolated sound sources and a far-field mixture. The algorithm is formulated as adaptive convolutive filtering in short-time Fourier transform (STFT) domain. We use the recursive least squares (RLS) algorithm for estimating the filter parameters due to its fast convergence rate, which is required for modeling rapidly changing RIRs of moving sound sources. The proposed method allows separation of reverberated sources from the far-field mixture given that their close-field signals are available. The evaluation is based on measuring unmixing performance (removal of reverberated source) using objective separation criteria calculated between the ground truth recording of the preserved sources and the unmixing result obtained with the proposed algorithm. We compare online and offline formulations for the RIR estimation and also provide evaluation with blind source separation algorithm only operating on the mixture signal.

This paper presents an algorithm for multichannel sound source separation using explicit modeling of level and time differences in source spatial covariance matrices (SCM). We propose a novel SCM model in which the spatial properties are modeled by the weighted sum of direction of arrival (DOA) kernels. DOA kernels are obtained as the combination of phase and level difference covariance matrices representing both time and level differences between microphones for a grid of predefined source directions. The proposed SCM model is combined with the NMF model for the magnitude spectrograms. Opposite to other SCM models in the literature, in this work, source localization is implicitly defined in the model and estimated during the signal factorization. Therefore, no localization preprocessing is required. Parameters are estimated using complex-valued nonnegative matrix factorization with both Euclidean distance and Itakura-Saito divergence. Separation performance of the proposed system is evaluated using the two-channel SiSEC development dataset and four channels signals recorded in a regular room with moderate reverberation. Finally, a comparison to other state-of-the-art methods is performed, showing better achieved separation performance in terms of SIR and perceptual measures.

In this paper, we propose a method for separation of moving sound sources. The method is based on first tracking the sources and then estimation of source spectrograms using multichannel nonnegative matrix factorization (NMF) and extracting the sources from the mixture by single-channel Wiener filtering. We propose a novel multichannel NMF model with time-varying mixing of the sources denoted by spatial covariance matrices (SCM) and provide update equations for optimizing model parameters minimizing squared Frobenius norm. The SCMs of the model are obtained based on estimated directions of arrival of tracked sources at each time frame. The evaluation is based on established objective separation criteria and using real recordings of two and three simultaneous moving sound sources. The compared methods include conventional beamforming and ideal ratio mask separation. The proposed method is shown to exceed the separation quality of other evaluated blind approaches according to all measured quantities. Additionally, we evaluate the method's susceptibility toward tracking errors by comparing the separation quality achieved using annotated ground truth source trajectories.

This paper studies multichannel audio separation using non-negative matrix factorization (NMF) combined with a new model for spatial covariance matrices (SCM). The proposed model for SCMs is parameterized by source direction of arrival (DoA) and its parameters can be optimized to yield a spatially coherent solution over frequencies thus avoiding permutation ambiguity and spatial aliasing. The model constrains the estimation of SCMs to a set of geometrically possible solutions. Additionally we present a method for using a priori DoA information of the sources extracted blindly from the mixture for the initialization of the parameters of the proposed model. The simulations show that the proposed algorithm exceeds the separation quality of existing spatial separation methods.

High level of noise reduces the perceptual quality and intelligibility of speech. Therefore, enhancing the captured speech signal is important in everyday applications such as telephony and teleconferencing. Microphone arrays are typically placed at a distance from a speaker and require processing to enhance the captured signal. Beamforming provides directional gai nt owards the source of interest and attenuation of interference. It is often followed by a single channel post-filter to further enhance the signal. Non-linear spatial post-filters are capable of providing high noise suppression but can produce unwanted musical noise that lowers the perceptual quality of the output. This work proposes an artificial neural network (ANN) to learn the structure of naturally occurring post-filters to enhance speech from interfering noise. The ANN uses phase-based features obtained from a multichannel array as an input. Simulations are used to train the ANN in a supervised manner. The performance is measured with objective scores from speech recorded in an office environment. The post-filters predicted by the ANN are found to improve the perceptual quality over delay-and-sum beamforming while maintaining high suppression of noise characteristic to spatial post-filters. Index Terms:Speech enhancement, Microphone arrays, Array signal processing, Artificial neural networks, Psychoacoustics.

This paper addresses the problem of sound source separation from a multichannel microphone array capture via estimation of source spatial covariance matrix (SCM) of a short-time Fourier transformed mixture signal. In many conventional audio separation algorithms the source mixing parameter estimation is done separately for each frequency thus making them prone to errors and leading to suboptimal source estimates. In this paper we propose a SCM model which consists of a weighted sum of direction of arrival (DoA) kernels and estimate only the weights dependent on the source directions. In the proposed algorithm, the spatial properties of the sources become jointly optimized over all frequencies, leading to more coherent source estimates and mitigating the effect of spatial aliasing at high frequencies. The proposed SCM model is combined with a linear model for magnitudes and the parameter estimation is formulated in a complex-valued non-negative matrix factorization (CNMF) framework. Simulations consist of recordings done with a hand-held device sized array having multiple microphones embedded inside the device casing. Separation quality of the proposed algorithm is shown to exceed the performance of existing state of the art separation methods with two sources when evaluated by objective separation quality metrics.

This paper proposes a novel algorithm for minimizing the perceptual distortion in non-negative matrix factorization (NMF) based audio representation. We formulate the noise-to-mask ratio audio quality criterion in a form where it can be used in NMF and propose an algorithm for optimizing the criterion. We also propose a method for compensating the spreading of the representation error in the synthesis filterbank. The objective perceptual quality produced by the proposed method is found to outperform all the reference methods. We also study the trade-off between the window length and the rank of factorization with a fixed data rate, and find that the best performance is obtained with window lengths between 10 and 30 ms.