Blind signal separation

This paper presents a new blind speech separation algorithm using beamforming technique that is capable of extracting each individual speech signal from a mixture of three speech sources in a room.

10p vithanos2711 09-08-2019 24 1   Download

This paper proposes a new method to address the problem of blind speech separation in convolutive mixtures in the time domain. The main idea is extract the innovation processes of speech sources by nonGaussianity maximization and then artificially color them by re-coloration filters.

8p minhxaminhyeu3 25-06-2019 19 0   Download

This paper addresses the problem of blind separation of non stationary signals. We introduce an online separating algorithm for estimation of independent source signals using the assumption of non-stationarity of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter.

17p vinguyentuongdanh 20-12-2018 21 0   Download

This paper investigates the problem of speech separation from a mixture of two speech signals without source localization information in a room environment. Due to the lack of source information, the use of spatial detector comes at an expense of permutation ambiguity. To solve the problem, a permutation alignment algorithm based on correlation is employed to group the beamformer outputs into the correct sources.

11p dannisa 14-12-2018 27 1   Download

Data is increasingly generated by instruments that monitor the environment – telescopes looking at the heavens, DNA sequenc- ers decoding molecules, bar-code readers watching passing freight-cars, patient monitors watching the life-signs of a person in the emergency room, cell-phone and credit-card systems look- ing for fraud, RFID scanners watching products flow through the supply chain, and smart-dust sensing its environment. In each of these cases, one wants to compare...

15p yasuyidol 02-04-2013 64 4   Download

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition.

576p kimngan_1 06-11-2012 55 8   Download

Independent Component Analysis (ICA) is a signal-processing method to extract independent sources given only observed data that are mixtures of the unknown sources. Recently, Blind Source Separation (BSS) by ICA has received considerable attention because of its potential signal-processing applications such as speech enhancement systems, image processing, telecommunications, medical signal processing and several data mining issues.

0p cucdai_1 19-10-2012 61 7   Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Blind I/Q Signal Separation-Based Solutions for Receiver Signal Processing

11p dauphong20 11-03-2012 30 4   Download

EURASIP Journal on Applied Signal Processing 2003:11, 1135–1146 c 2003 Hindawi Publishing Corporation Blind Source Separation Combining Independent Component Analysis and Beamforming Hiroshi Saruwatari Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan Email: sawatari@is.aist-nara.ac.

12p sting12 10-03-2012 41 3   Download

EURASIP Journal on Applied Signal Processing 2003:11, 1157–1166 c 2003 Hindawi Publishing Corporation Equivalence between Frequency-Domain Blind Source Separation and Frequency-Domain Adaptive Beamforming for Convolutive Mixtures Shoko Araki NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan Email: shoko@cslab.kecl.ntt.co.jp Shoji Makino NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan Email: maki@cslab.kecl.ntt.co.

10p sting12 10-03-2012 45 4   Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Tracking Signal Subspace Invariance for Blind Separation and Classification of Nonorthogonal Sources in Correlated Noise

20p dauphong20 10-03-2012 35 2   Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Fixed-Point Algorithms for the Blind Separation of Arbitrary Complex-Valued Non-Gaussian Signal Mixtures

15p dauphong20 10-03-2012 46 4   Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Frequency-Domain Blind Source Separation of Many Speech Signals Using Near-Field and Far-Field Models

13p dauphong20 10-03-2012 40 2   Download

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Blind Separation of Acoustic Signals Combining SIMO-Model-Based Independent Component Analysis and Binary Masking

17p sting11 09-03-2012 36 5   Download

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: Research Article Combining Superdirective Beamforming and Frequency-Domain Blind Source Separation for Highly Reverberant Signals

13p dauphong15 16-02-2012 62 7   Download

Telecommunications This chapter deals with applications of independent component analysis (ICA) and blind source separation (BSS) methods to telecommunications. In the following, we concentrate on code division multiple access (CDMA) techniques, because this specific branch of telecommunications provides several possibilities for applying ICA and BSS in a meaningful way. After an introduction to multiuser detection and CDMA communications, we present mathematically the CDMA signal model and show that it can be cast in the form of a noisy matrix ICA model.

24p khinhkha 29-07-2010 76 3   Download

Convolutive Mixtures and Blind Deconvolution This chapter deals with blind deconvolution and blind separation of convolutive mixtures. Blind deconvolution is a signal processing problem that is closely related to basic independent component analysis (ICA) and blind source separation (BSS). In communications and related areas, blind deconvolution is often called blind equalization. In blind deconvolution, we have only one observed signal (output) and one source signal (input). The observed signal consists of an unknown source signal mixed with itself at different time delays.

16p khinhkha 29-07-2010 76 8   Download

This chapter deals with blind deconvolution and blind separation of convolutive mixtures. Blind deconvolution is a signal processing problem that is closely related to basic independent component analysis (ICA) and blind source separation (BSS). In communications and related areas, blind deconvolution is often called blind equalization. In blind deconvolution, we have only one observed signal (output) and one source signal (input).

16p duongph05 09-06-2010 89 8   Download

Speech separation from noise, given a-priori information, can be viewed as a subspace estimation problem. Some conventional speech enhancement methods are spectral subtraction [1], Wiener filtering [2], blind signal separation [3] and hidden Markov modelling [4]. Hidden Markov Model (HMM) based speech enhancement techniques are related to the problem of performing speech recognition in noisy environments [5,6]. HMM based methods uses a-priori information about both the speech and the noise [4]. Some papers propose HMM speech enhancement techniques applied to stationary noise sources [4,7]....

30p longmontran 18-01-2010 108 18   Download