Ronen Talmon

Supervised source localization using diffusion kernels

Recently, we introduced a method to recover the controlling parameters of linear systems using diffusion kernels. In this paper, we apply our approach to the problem of source localization in a reverberant room using measurements from a single microphone. Prior recordings of signals from various known locations in the room are required for training and calibration. The proposed algorithm relies on a computation of a diffusion kernel with a specially-tailored distance measure. Experimental results in a real reverberant environment demonstrate accurate recovery of the source location.

Clustering and suppression of transient noise in speech signals using diffusion maps

Recently we have presented a novel approach for transient noise reduction that relies on non-local (NL) filtering. In this paper, we modify and extend our approach to support clustering and suppression of a few transient noise types simultaneously, by introducing two novel concepts. We observe that voiced speech spectral components are slowly varying compared to transient noise. Thus, by applying an algorithm for noise power spectral density (PSD) estimation, configured to track faster variations than pseudo-stationary noise, the PSD of speech components may be estimated. In addition, we utilize diffusion maps to embed the measurements into a new domain. We obtain a new representation which enables clustering of different transient noise types. The new representation is incorporated into a NL filter as a better affinity metric for averaging over transient instances. Experimental results show that the proposed algorithm enables clustering and suppression of multiple transient interferences.

Speech enhancement in transient noise environment using diffusion filtering

Recently, we have presented a transient noise reduction algorithm for speech signals that relies on non-local diffusion filtering. By exploiting the repetitive nature of transient noises we proposed a simple and efficient algorithm, which enabled suppression of various noise types. In this paper, we incorporate a modified diffusion operator in order to obtain a more robust algorithm and further enhancement of the speech. We demonstrate the performance of the modified algorithm and compare it with a competing solution. We show that the proposed algorithm enables improved suppression of various transient interferences without any further computational burden.

Alternating diffusion for common manifold learning with application to sleep stage assessment

In this paper, we address the problem of multimodal signal processing and present a manifold learning method to extract the common source of variability from multiple measurements. This method is based on alternating-diffusion and is particularly adapted to time series. We show that the common source of variability is extracted from multiple sensors as if it were the only source of variability, extracted by a standard manifold learning method from a single sensor, without the influence of the sensor-specific variables. In addition, we present application to sleep stage assessment. We demonstrate that, indeed, through alternating-diffusion, the sleep information hidden inside multimodal respiratory signals can be better captured compared to single-modal methods.

Blind reverberation time estimation by intrinsic modeling of reverberant speech

The reverberation time (RT) is a very important measure that quantifies the acoustic properties of a room and provides information about the quality and intelligibility of speech recorded in that room. Moreover, information about the RT can be used to improve the performance of automatic speech recognition systems and speech dereverberation algorithms. In a recent study, it has been shown that existing methods for blind estimation of the RT are highly sensitive to additive noise. In this paper, a novel method is proposed to blindly estimate the RT based on the decay rate distribution. Firstly, a data-driven representation of the underlying decay rates of several training rooms is obtained via the eigenvalue decomposition of a specially-tailored kernel. Secondly, the representation is extended to a room under test and used to estimate its decay rate (and hence its RT). The presented results show that the proposed method outperforms a competing method and is significantly more robust to noise.

A Study on Manifolds of Acoustic Responses

The construction of a meaningful metric between acoustic responses which respects the source locations, is addressed. By comparing three alternative distance measures, we verify the existence of the acoustic manifold and give an insight into its nonlinear structure. From such a geometric view point, we demonstrate the limitations of linear approaches to infer physical adjacencies. Instead, we introduce the diffusion framework, which combines local and global processing in order to find an intrinsic nonlinear embedding of the data on a low-dimensional manifold. We present the diffusion distance which is related to the geodesic distance on the manifold. In particular, simulation results demonstrate the ability of the diffusion distance to organize the samples according to the source direction of arrival DOA.

Multichannel speech enhancement using convolutive transfer function approximation in reverberant environments

Recently, we have presented a transfer-function generalized sidelobe canceler (TF-GSC) beamformer in the short time Fourier transform domain, which relies on a convolutive transfer function approximation of relative transfer functions between distinct sensors. In this paper, we combine a delay-and-sum beamformer with the TF-GSC structure in order to suppress the speech signal reflections captured at the sensors in reverberant environments. We demonstrate the performance of the proposed beamformer and compare it with the TF-GSC. We show that the proposed algorithm enables suppression of reverberations and further noise reduction compared with the TF-GSC beamformer.

Supervised system identification based on local PCA models

We propose a supervised system identification method for recovering an acoustic impulse response in a reverberant room. Unlike most existing methods, our algorithm is based on prior information given in the form of a training set of known impulse responses acquired in a controlled environment. By relying on the prior information, we train local Principal Component Analysis (PCA) models of impulse responses corresponding to several different regions in the room. We propose to crudely localize the respective source position, and subsequently, based on the appropriate local model, recover the impulse response. In order to approximate the source location, we introduce a specially-tailored distance measure which is based on an affinity between the trained local models. Experimental results in simulated noisy and reverberant environments demonstrate significant improvements over existing methods.

Identification of the relative transfer function between microphones in reverberant environments

Recently, a relative transfer function (RTF) identification method based on the convolutive transfer function (CTF) approximation was developed. This method is adapted to speech sources in reverberant environments and exploits the non-stationarity and presence probability of the speech signal. In this paper, we present experimental results that demonstrate the advantages and robustness of the proposed method. Specifically, we show the robustness of this method to the environment and to a variety of recorded noise signals.

Speaker Tracking on Multiple-Manifolds with Distributed Microphones

Speaker tracking in a reverberant enclosure with an ad hoc network of multiple distributed microphones is addressed in this paper. A set of prerecorded measurements in the enclosure of interest is used to construct a data-driven statistical model. The function mapping the measurement-based features to the corresponding source position represents complex unknown relations, hence it is modelled as a random Gaussian process. The process is defined by a covariance function which encapsulates the relations among the available measurements and the different views presented by the distributed microphones. This model is intertwined with a Kalman filter to capture both the smoothness of the source movement in the time-domain and the smoothness with respect to patterns identified in the set of available prerecorded measurements. Simulation results demonstrate the ability of the proposed method to localize a moving source in reverberant conditions.