413,771 research outputs found

    An investigation of discrete-state discriminant approaches to single-sensor source separation

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    International audienceThis paper investigated a new scheme for single-sensor audio source separation. This framework is introduced comparatively to the existing Gaussian mixture model generative approach and is focusing on the mixture states rather than on the source states, resulting in a discrete, joint state discriminant approach. The study establishes the theoretical performance bounds of the proposed scheme and an actual source separation system is designed. The performance is computed on a set of musical recordings and a discussion is proposed, including the question of the source correlation and the possible drawbacks of the method

    Single Channel Sound Source Separation Combining Delay Estimation and the AdRess Algorithm

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    A method for single channel source separation is proposed in this paper, which uses estimates fo the delay co-efficient of individual sources within an echoic mixture using autocorrelation, following which a pseudo-stereo mixture is generated, to which the ADRess algorithm can be applied. The system is evaluated in a theoretical situation, where the mixture signal to be separated consists of two individual source signals, and a delayed version of each signal. Estimates of the individual delay lengths are made and then used to create a pseudo stereo mix, where one channel consists of the orginal mixture signal, and the second channel consists of the original signal shifted by the length of the delay calculated for each source. The ADRess algorithm is then used to separate sources from the new pseudo stereo mixture

    Statistical single channel source separation

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    PhD ThesisSingle channel source separation (SCSS) principally is one of the challenging fields in signal processing and has various significant applications. Unlike conventional SCSS methods which were based on linear instantaneous model, this research sets out to investigate the separation of single channel in two types of mixture which is nonlinear instantaneous mixture and linear convolutive mixture. For the nonlinear SCSS in instantaneous mixture, this research proposes a novel solution based on a two-stage process that consists of a Gaussianization transform which efficiently compensates for the nonlinear distortion follow by a maximum likelihood estimator to perform source separation. For linear SCSS in convolutive mixture, this research proposes new methods based on nonnegative matrix factorization which decomposes a mixture into two-dimensional convolution factor matrices that represent the spectral basis and temporal code. The proposed factorization considers the convolutive mixing in the decomposition by introducing frequency constrained parameters in the model. The method aims to separate the mixture into its constituent spectral-temporal source components while alleviating the effect of convolutive mixing. In addition, family of Itakura-Saito divergence has been developed as a cost function which brings the beneficial property of scale-invariant. Two new statistical techniques are proposed, namely, Expectation-Maximisation (EM) based algorithm framework which maximizes the log-likelihood of a mixed signals, and the maximum a posteriori approach which maximises the joint probability of a mixed signal using multiplicative update rules. To further improve this research work, a novel method that incorporates adaptive sparseness into the solution has been proposed to resolve the ambiguity and hence, improve the algorithm performance. The theoretical foundation of the proposed solutions has been rigorously developed and discussed in details. Results have concretely shown the effectiveness of all the proposed algorithms presented in this thesis in separating the mixed signals in single channel and have outperformed others available methods.Universiti Teknikal Malaysia Melaka(UTeM), Ministry of Higher Education of Malaysi

    Gaussian mixture gain priors for regularized nonnegative matrix factorization in single-channel source separation

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    We propose a new method to incorporate statistical priors on the solution of the nonnegative matrix factorization (NMF) for single-channel source separation (SCSS) applications. The Gaussian mixture model (GMM) is used as a log-normalized gain prior model for the NMF solution. The normalization makes the prior models energy independent. In NMF based SCSS, NMF is used to decompose the spectra of the observed mixed signal as a weighted linear combination of a set of trained basis vectors. In this work, the NMF decomposition weights are enforced to consider statistical prior information on the weight combination patterns that the trained basis vectors can jointly receive for each source in the observed mixed signal. The NMF solutions for the weights are encouraged to increase the loglikelihood with the trained gain prior GMMs while reducing the NMF reconstruction error at the same time

    Single channel sound source separation combining delay estimation and the ADRess algorithm

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    A method for single channel source separation is proposed in this paper, which uses estimates fo the delay co-efficient of individual sources within an echoic mixture using autocorrelation, following which a pseudo-stereo mixture is generated, to which the ADRess algorithm can be applied. The system is evaluated in a theoretical situation, where the mixture signal to be separated consists of two individual source signals, and a delayed version of each signal. Estimates of the individual delay lengths are made and then used to create a pseudo stereo mix, where one channel consists of the orginal mixture signal, and the second channel consists of the original signal shifted by the length of the delay calculated for each source. The ADRess algorithm is then used to separate sources from the new pseudo stereo mixture

    Non-Negative Matrix Factorization Based Algorithms to Cluster Frequency Basis Functions for Monaural Sound Source Separation.

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    Monophonic sound source separation (SSS) refers to a process that separates out audio signals produced from the individual sound sources in a given acoustic mixture, when the mixture signal is recorded using one microphone or is directly recorded onto one reproduction channel. Many audio applications such as pitch modification and automatic music transcription would benefit from the availability of segregated sound sources from the mixture of audio signals for further processing. Recently, Non-negative matrix factorization (NMF) has found application in monaural audio source separation due to its ability to factorize audio spectrograms into additive part-based basis functions, where the parts typically correspond to individual notes or chords in music. An advantage of NMF is that there can be a single basis function for each note played by a given instrument, thereby capturing changes in timbre with pitch for each instrument or source. However, these basis functions need to be clustered to their respective sources for the reconstruction of the individual source signals. Many clustering methods have been proposed to map the separated signals into sources with considerable success. Recently, to avoid the need of clustering, Shifted NMF (SNMF) was proposed, which assumes that the timbre of a note is constant for all the pitches produced by an instrument. SNMF has two drawbacks. Firstly, the assumption that the timbre of the notes played by an instrument remains constant, is not true in general. Secondly, the SNMF method uses the Constant Q transform (CQT) and the lack of a true inverse of the CQT results in compromising on separation quality of the reconstructed signal. The principal aim of this thesis is to attempt to solve the problem of clustering NMF basis functions. Our first major contribution is the use of SNMF as a method of clustering the basis functions obtained via standard NMF. The proposed SNMF clustering method aims to cluster the frequency basis functions obtained via standard NMF to their respective sources by making use of shift invariance in a log-frequency domain. Further, a minor contribution is made by improving the separation performance of the standard SNMF algorithm (here used directly to separate sources) obtained through the use of an improved inverse CQT. Here, the standard SNMF algorithm finds shift-invariance in a CQ spectrogram, that contain the frequency basis functions, obtained directly from the spectrogram of the audio mixture. Our next contribution is an improvement in the SNMF clustering algorithm through the incorporation of the CQT matrix inside the SNMF model in order to avoid the need of an inverse CQT to reconstruct the clustered NMF basis unctions. Another major contribution deals with the incorporation of a constraint called group sparsity (GS) into the SNMF clustering algorithm at two stages to improve clustering. The effect of the GS is evaluated on various SNMF clustering algorithms proposed in this thesis. Finally, we have introduced a new family of masks to reconstruct the original signal from the clustered basis functions and compared their performance to the generalized Wiener filter masks using three different factorisation-based separation algorithms. We show that better separation performance can be achieved by using the proposed family of masks

    Counter-current chromatography for the separation of terpenoids: A comprehensive review with respect to the solvent systems employed

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    Copyright @ 2014 The Authors.This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.Natural products extracts are commonly highly complex mixtures of active compounds and consequently their purification becomes a particularly challenging task. The development of a purification protocol to extract a single active component from the many hundreds that are often present in the mixture is something that can take months or even years to achieve, thus it is important for the natural product chemist to have, at their disposal, a broad range of diverse purification techniques. Counter-current chromatography (CCC) is one such separation technique utilising two immiscible phases, one as the stationary phase (retained in a spinning coil by centrifugal forces) and the second as the mobile phase. The method benefits from a number of advantages when compared with the more traditional liquid-solid separation methods, such as no irreversible adsorption, total recovery of the injected sample, minimal tailing of peaks, low risk of sample denaturation, the ability to accept particulates, and a low solvent consumption. The selection of an appropriate two-phase solvent system is critical to the running of CCC since this is both the mobile and the stationary phase of the system. However, this is also by far the most time consuming aspect of the technique and the one that most inhibits its general take-up. In recent years, numerous natural product purifications have been published using CCC from almost every country across the globe. Many of these papers are devoted to terpenoids-one of the most diverse groups. Naturally occurring terpenoids provide opportunities to discover new drugs but many of them are available at very low levels in nature and a huge number of them still remain unexplored. The collective knowledge on performing successful CCC separations of terpenoids has been gathered and reviewed by the authors, in order to create a comprehensive document that will be of great assistance in performing future purifications. © 2014 The Author(s)
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