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Sounds from Single-Channel Mixtures

by Paris Smaragdis, Bhiksha Raj, Madhusudana Shashanka , 2006
"... In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two scenari ..."
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In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in two

Supervised and Semi-Supervised Separation of Sounds from Single-Channel Mixtures

by Paris Smaragdis, Bhiksha Raj, Madhusudana Shashanka
"... Abstract. In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures in t ..."
Abstract - Cited by 51 (13 self) - Add to MetaCart
Abstract. In this paper we describe a methodology for model-based single channel separation of sounds. We present a sparse latent variable model that can learn sounds based on their distribution of time/frequency energy. This model can then be used to extract known types of sounds from mixtures

Source extraction from two-channel mixtures by joint cosine packet analysis

by Andrew Nesbit, Mike Davies, Mark Plumbley, Mark S - in Proc. European Signal Processing Conf. (EUSIPCO , 2006
"... This paper describes novel, computationally efficient approaches to source separation of underdetermined instantaneous two-channel mixtures. A best basis algorithm is applied to trees of local cosine bases to determine a sparse transform. We assume that the mixing parameters are known and focus on d ..."
Abstract - Cited by 3 (3 self) - Add to MetaCart
This paper describes novel, computationally efficient approaches to source separation of underdetermined instantaneous two-channel mixtures. A best basis algorithm is applied to trees of local cosine bases to determine a sparse transform. We assume that the mixing parameters are known and focus

Single-channel mixture decomposition using Bayesian harmonic models

by Emmanuel Vincent, Mark D. Plumbley - in Proc. ICA, 2006 , 2006
"... Abstract. We consider the source separation problem for single-channel music signals. After a brief review of existing methods, we focus on decomposing a mixture into components made of harmonic sinusoidal partials. We address this problem in the Bayesian framework by building a probabilistic model ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Abstract. We consider the source separation problem for single-channel music signals. After a brief review of existing methods, we focus on decomposing a mixture into components made of harmonic sinusoidal partials. We address this problem in the Bayesian framework by building a probabilistic model

Blind Separation of Single Channel Mixture Using ICA Basis Functions

by Gil-jin Jang, Te-won Lee, Yung-hwan Oh - 3rd International Conference on Independent Component Analysis and Blind Signal Separation , 2001
"... A new technique has been developed to enable blind source separation given only a single channel recording. The proposed method infers source signals and their contribution factors at each time point by a number of adaptation steps maximizing log-likelihood of the estimated source parameters given t ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
A new technique has been developed to enable blind source separation given only a single channel recording. The proposed method infers source signals and their contribution factors at each time point by a number of adaptation steps maximizing log-likelihood of the estimated source parameters given

2003 IEEE Workshop an Applications of Signal Prwessinp to Audio and Acoustics October 19-?2.ZuO3, New Pal=. NY SEPARATION OF HARMONIC INSTRUMENTS WITH OVERLAPPING PARTIALS IN MULTI-CHANNEL MIXTURES

by Harald Viste, Gianpaolo Evangelista
"... When instruments play together, different partials will often overlap in time and frequency. This is particularly likely for harmonic instruments. We present a new method for the separation of overlapping partials in multi-channel mixtures. The method is based on the obsewation that when a harmonic ..."
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When instruments play together, different partials will often overlap in time and frequency. This is particularly likely for harmonic instruments. We present a new method for the separation of overlapping partials in multi-channel mixtures. The method is based on the obsewation that when a harmonic

Mixture

by Ron Weiss
"... • Have a monoaural signal composed of multiple sources • e.g. multiple speakers, speech + music, speech + background noise • Want to separate the constituent sources • For noise robust speech recognition, hearing aidsA Classi↓cation Approach to Single Channel Source Separation – p. 3/8 ..."
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• Have a monoaural signal composed of multiple sources • e.g. multiple speakers, speech + music, speech + background noise • Want to separate the constituent sources • For noise robust speech recognition, hearing aidsA Classi↓cation Approach to Single Channel Source Separation – p. 3/8

Nonlinear source separation: the post-nonlinear mixtures

by Anisse Taleb, Christian Jutten - In: Proceedings of the ESANN’97 , 1997
"... Abstract—In this paper, we address the problem of separation of mutually independent sources in nonlinear mixtures. First, we propose theoretical results and prove that in the general case, it is not possible to separate the sources without nonlinear distortion. Therefore, we focus our work on speci ..."
Abstract - Cited by 148 (26 self) - Add to MetaCart
on specific nonlinear mixtures known as post-nonlinear mixtures. These mixtures constituted by a linear instantaneous mixture (linear memoryless channel) followed by an unknown and invertible memoryless nonlinear distortion, are realistic models in many situations and emphasize interesting properties i

Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria

by Tuomas Virtanen - IEEE Trans. On Audio, Speech and Lang. Processing , 2007
"... Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain ..."
Abstract - Cited by 189 (30 self) - Add to MetaCart
Abstract—An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time

Channel compensation for SVM speaker recognition

by Alex Solomonoff, Carl Quillen, William M. Campbell - in Proceedings of Odyssey-04, The Speaker and Language Recognition Workshop
"... One of the major remaining challenges to improving accuracy in state-of-the-art speaker recognition algorithms is reducing the impact of channel and handset variations on system performance. For Gaussian Mixture Model based speaker recognition systems, a variety of channel-adaptation techniques are ..."
Abstract - Cited by 113 (16 self) - Add to MetaCart
One of the major remaining challenges to improving accuracy in state-of-the-art speaker recognition algorithms is reducing the impact of channel and handset variations on system performance. For Gaussian Mixture Model based speaker recognition systems, a variety of channel-adaptation techniques
Results 1 - 10 of 1,309