https://repository.usfca.edu/cgi/viewcontent.cgi?article=1091&context=nursing_fac

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks.

/pdf/a-survey-of-convolutive-blind-source-separation-methods-1tb18ec3v1.pdf

A survey of convolutive blind source separation methods

In speech communication systems, such as voice-controlled systems, hands-free mobile telephones, and hearing aids, the received microphone signals are degraded by room reverberation, background noise, and other interferences This signal degradation may lead to total unintelligibility of the speech and decreases the performance of automatic speech recognition systems In the context of this work reverberation is the process of multi-path propagation of an acoustic sound from its source to one or more microphones The received microphone signal generally consists of a direct sound, reflections that arrive shortly after the direct sound (commonly called early reverberation), and reflections that arrive after the early reverberation (commonly called late reverberation) Reverberant speech can be described as sounding distant with noticeable echo and colouration These detrimental perceptual effects are primarily caused by late reverberation, and generally increase with increasing distance between the source and microphone Conversely, early reverberations tend to improve the intelligibility of speech In combination with the direct sound it is sometimes referred to as the early speech component Reduction of the detrimental effects of reflections is evidently of considerable practical importance, and is the focus of this dissertation More specifically the dissertation deals with dereverberation techniques, ie, signal processing techniques to reduce the detrimental effects of reflections In the dissertation, novel single- and multimicrophone speech dereverberation algorithms are developed that aim at the suppression of late reverberation, ie, at estimation of the early speech component This is done via so-called spectral enhancement techniques that require a specific measure of the late reverberant signal This measure, called spectral variance, can be estimated directly from the received (possibly noisy) reverberant signal(s) using a statistical reverberation model and a limited amount of a priori knowledge about the acoustic channel(s) between the source and the microphone(s) In our work an existing single-channel statistical reverberation model serves as a starting point The model is characterized by one parameter that depends on the acoustic characteristics of the environment We show that the spectral variance estimator that is based on this model, can only be used when the source-microphone distance is larger than the so-called critical distance This is, crudely speaking, the distance where the direct sound power is equal to the total reflective power A generalization of the statistical reverberation model in which the direct sound is incorporated is developed This model requires one additional parameter that is related to the ratio between the direct sound energy and the sound energy of all reflections The generalized model is used to derive a novel spectral variance estimator When the novel estimator is used for dereverberation rather than the existing estimator, and the source-microphone distance is smaller than the critical distance, the dereverberation performance is significantly increased Single-microphone systems only exploit the temporal and spectral diversity of the received signal Reverberation, of course, also induces spatial diversity To additionally exploit this diversity, multiple microphones must be used, and their outputs must be combined by a suitable spatial processor such as the so-called delay and sum beamformer It is not a priori evident whether spectral enhancement is best done before or after the spatial processor For this reason we investigate both possibilities, as well as a merge of the spatial processor and the spectral enhancement technique An advantage of the latter option is that the spectral variance estimator can be further improved Our experiments show that the use of multiple microphones affords a significant improvement of the perceptual speech quality The applicability of the theory developed in this dissertation is demonstrated using a hands-free communication system Since hands-free systems are often used in a noisy and reverberant environment, the received microphone signal does not only contain the desired signal but also interferences such as room reverberation that is caused by the desired source, background noise, and a far-end echo signal that results from a sound that is produced by the loudspeaker Usually an acoustic echo canceller is used to cancel the far-end echo Additionally a post-processor is used to suppress background noise and residual echo, ie, echo which could not be cancelled by the echo canceller In this work a novel structure and post-processor for an acoustic echo canceller are developed The post-processor suppresses late reverberation caused by the desired source, residual echo, and background noise The late reverberation and late residual echo are estimated using the generalized statistical reverberation model Experimental results convincingly demonstrate the benefits of the proposed system for suppressing late reverberation, residual echo and background noise The proposed structure and post-processor have a low computational complexity, a highly modular structure, can be seamlessly integrated into existing hands-free communication systems, and affords a significant increase of the listening comfort and speech intelligibility

/pdf/single-and-multi-microphone-speech-dereverberation-using-81pl8h93qv.pdf

Single- and multi-microphone speech dereverberation using spectral enhancement

In this paper, we propose a new frequency domain approach to blind source separation (BSS) of audio signals mixed in a reverberant environment. We propose a joint diagonalization procedure on the cross power spectral density matrices of the signals at the output of the mixing system to identify the mixing system at each frequency bin up to a scale and permutation ambiguity. The frequency domain joint diagonalization is performed using a new and quickly converging algorithm which uses an alternating least-squares (ALS) optimization method. The inverse of the mixing system is then used to separate the sources. An efficient dyadic algorithm to resolve the frequency dependent permutation ambiguities that exploits the inherent nonstationarity of the sources is presented. The effect of the unknown scaling ambiguities is partially resolved using an initialization procedure for the ALS algorithm. The performance of the proposed algorithm is demonstrated by experiments conducted in real reverberant rooms. Performance comparisons are made with previous methods.

A frequency domain method for blind source separation of convolutive audio mixtures

A novel triple-image encryption and hiding algorithm based on chaos, compressive sensing and 3D DCT

Patients that undergo treatment in the epilepsy clinic Kempenhaeghe in the Netherlands are being monitored with different sensory signals, including audio. In this paper a new patient monitoring system for the detection of epileptic seizures through audio classification is proposed. The proposed system enables automated detection of epileptic seizures, which can have a large positive impact on the daily care of epilepsy patients. This system includes three stages. First, the signal is enhanced by means of a microphone array, followed by a noise subtraction procedure. Secondly, the signal is analyzed by audio event detection and audio classification. When an audio event is detected, features are extracted from the signal. Bayesian decision theory is used to classify the feature vector based on a discriminant analysis. Finally, it is decided whether or not to trigger an alarm. The performance is tested with audio signals obtained from measurements with epileptic patients. The results show that, with a limited set of features, good classification results can be achieved.

Detection of Epileptic Seizures Through Audio Classification

This paper addresses the problem of separating multiple speakers from mixtures of these that are obtained using multiple microphones in a room. A new adaptive blind signal separation algorithm is derived which is entirely based on Second Order Statistics which is entitled CoBliSS. The CoBliSS algorithm can run in offline or online (adaptive) mode. One of the advantages of the CoBliSS algorithm is that no assumptions are made about the probability density functions or other properties of the signals. Experiments with real recordings were carried out in a normal living room which show that the algorithm has good performance. As opposed to most other algorithms no parameters need to be tuned.

/pdf/a-new-blind-signal-separation-algorithm-based-on-second-3d111givl4.pdf

A new blind signal separation algorithm based on second-order statistics

Blind identification is a crucial subtask in signal processing problems such as blind signal separation (BSS) and direction-of-arrival (DOA) estimation This paper presents a procedure for multiple-input multiple-output instantaneous blind identification based on second-order temporal properties of the signals, such as coloredness and nonstationarity The procedure consists of two stages First, based on assumptions on the second-order temporal structure (SOTS) of the source and noise signals, and using subspace techniques, the problem is reformulated in a particular way such that each column of the unknown mixing matrix satisfies a system of multivariate homogeneous polynomial equations Then, this nonlinear system of equations is solved by means of a so-called homotopy method, which provides a general tool for solving (possibly nonexact) systems of nonlinear equations by smoothly deforming the known solutions of a simple start system into the desired solutions of the target system Our blind identification procedure allows to estimate the mixing matrix for scenarios with more sources than sensors without resorting to sparsity assumptions, something that is often believed to be impossible when using only second-order statistics In addition, since our algorithm does not require any assumption on the mixing matrix, also mixing matrices that are rank-deficient or even have identical columns can be identified Finally, we give examples and performance results for speech source signals

https://pure.tue.nl/ws/files/3024817/Metis221047.pdf

MIMO Instantaneous Blind Identification Based on Second-Order Temporal Structure

Performance analyses of adaptive algorithms such as LMS and RLS often rely on the assumption that the input signal is stationary. Also it often is assumed that the adaptive nite impulse response FIR filter is long enough to make a good approximation of the unknown system. In many practical situations these assumptions do not hold and the instantaneous misadjustment of the adaptive filter can grow large. In this paper this effect is investigated and two methods are presented to improve the performance of the adaptive filter.

/pdf/on-the-performance-of-too-short-adaptive-fir-filters-1sxrmrvzpz.pdf

Pcw Piet Sommen

Papers

Detection of Epileptic Seizures Through Audio Classification

A new blind signal separation algorithm based on second-order statistics

MIMO Instantaneous Blind Identification Based on Second-Order Temporal Structure

On the performance of too short adaptive FIR filters

Phantom sources applied to stereo-base widening using long frequency domain adaptive filters