What is the goal of sensory coding?
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Listening to two simultaneous speeches
Speech Communication
Blind Source Separation by Sparse Decomposition in a Signal Dictionary
Neural Computation
Learning Overcomplete Representations
Neural Computation
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Blind source separation-semiparametric statistical approach
IEEE Transactions on Signal Processing
Separation of speech from interfering sounds based on oscillatory correlation
IEEE Transactions on Neural Networks
Neural Computation
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Source separation with one ear: proposition for an anthropomorphic approach
EURASIP Journal on Applied Signal Processing
Separating mixed multi-component signal with an application in mechanical watch movement
Digital Signal Processing
Simple and powerful instrument model for the source separation of polyphonic music
WSEAS Transactions on Signal Processing
Single Channel Polyphonic Music Transcription
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Hybrid mammogram classification using rough set and fuzzy classifier
Journal of Biomedical Imaging
Single-channel speech separation based on long-short frame associated harmonic model
Digital Signal Processing
Perceptive, non-linear speech processing and spiking neural networks
Nonlinear Speech Modeling and Applications
Separation of human and animal seismic signatures using non-negative matrix factorization
Pattern Recognition Letters
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This paper presents a new technique for achieving blind signal separation when given only a single channel recording. The main concept is based on exploiting a prior sets of time-domain basis functions learned by independent component analysis (ICA) to the separation of mixed source signals observed in a single channel. The inherent time structure of sound sources is reflected in the ICA basis functions, which encode the sources in a statistically efficient manner. We derive a learning algorithm using a maximum likelihood approach given the observed single channel data and sets of basis functions. For each time point we infer the source parameters and their contribution factors. This inference is possible due to prior knowledge of the basis functions and the associated coefficient densities. A flexible model for density estimation allows accurate modeling of the observation and our experimental results exhibit a high level of separation performance for simulated mixtures as well as real environment recordings employing mixtures of two different sources.