A neural cocktail-party processor
Biological Cybernetics
A pitch determination and voiced/unvoiced decision algorithm for noisy speech
Speech Communication
Networks of spiking neurons: the third generation of neural network models
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Spatio-Temporal Pattern Recognition with Neural Networks: Application to Speech
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A maximum likelihood approach to single-channel source separation
The Journal of Machine Learning Research
Joint acoustic and modulation frequency
EURASIP Journal on Applied Signal Processing
Separation of speech from interfering sounds based on oscillatory correlation
IEEE Transactions on Neural Networks
Towards neurocomputational speech and sound processing
Progress in nonlinear speech processing
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We present an example of an anthropomorphic approach, in which auditory-based cues are combined with temporal correlation to implement a source separation system. The auditory features are based on spectral amplitude modulation and energy information obtained through 256 cochlear filters. Segmentation and binding of auditory objects are performed with a two-layered spiking neural network. The first layer performs the segmentation of the auditory images into objects, while the second layer binds the auditory objects belonging to the same source. The binding is further used to generate a mask (binary gain) to suppress the undesired sources from the original signal. Results are presented for a double-voiced (2 speakers) speech segment and for sentences corrupted with different noise sources. Comparative results are also given using PESQ (perceptual evaluation of speech quality) scores. The spiking neural network is fully adaptive and unsupervised.