Posts by Collection


Neural Offset Min-Sum Decoding

ISIT, 2017

This paper is about neural offset min-sum decoding (NOMS), a generalization of the offset min-sum algorithm used in practical channel decoders for LDPC codes.

Recommended citation: L. Lugosch and W. J. Gross, "Neural offset min-sum decoding," 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, 2017, pp. 1361-1365.

Deep Learning Methods for Improved Decoding of Linear Codes

IEEE Journal of Selected Topics in Signal Processing, 2018

A collaboration between researchers at Tel-Aviv University and McGill University describing a family of neural belief propagation algorithms for channel decoding.

Recommended citation: E. Nachmani, E. Marciano, L. Lugosch, W. J. Gross, D. Burshtein and Y. Be’ery, "Deep learning methods for improved decoding of linear codes," in IEEE Journal of Selected Topics in Signal Processing, special issue on "Machine Learning for Cognition in Radio Communications and Radar", vol. 12, no. 1, pp. 119-131, Feb. 2018.

Learning Algorithms for Error Correction

Masters thesis, 2018

Channel coding enables reliable communication over unreliable, noisy channels: by encoding messages with redundancy, it is possible to decode the messages in such a way that errors introduced by the channel are corrected. Modern channel codes achieve very low error rates at long block lengths, but long blocks are often not acceptable for low-latency applications. While there exist short block codes with excellent error-correction performance when decoded optimally, designing practical, low-complexity decoding algorithms that can achieve close-to-optimal results for short codes is still an open problem. In this thesis, we explore an approach to decoding short block codes in which the decoder is recast as a machine learning algorithm. After providing the background concepts on errorcorrecting codes and machine learning, we review the literature on learning algorithms for error correction, with a special emphasis on the recently introduced “neural belief propagation” algorithm. We then describe a set of modifications to neural belief propagation which improve its performance and reduce its implementation complexity. We also propose a new syndrome-based output layer for neural error-correcting decoders which takes the code structure into account during training to yield decoders with lower frame error rate. Finally, we suggest some future work.

Recommended citation: L. Lugosch, “Learning algorithms for error correction”, Masters thesis, McGill University, 2018.

Tone Recognition Using Lifters and CTC

Interspeech, 2018

An acoustic model for tonal languages that uses convolutional lifters operating on the cepstrogram representation of the input speech signal and CTC to map inputs to outputs to achieve better tone recognition performance.

Recommended citation: L. Lugosch and V. S. Tomar, “Tone recognition using lifters and CTC”, Interspeech, Hyderabad, India, pp. 2305-2309, September 2018.

Learning from the Syndrome

Asilomar, 2018

Use a differentiable relaxation of the syndrome as the loss function for training neural network channel decoders for improved frame error rate and online unsupervised learning. (Invited paper.)

Recommended citation: L. Lugosch, W. J. Gross, "Learning from the syndrome," in Asilomar Conference on Signals, Systems, and Computers, special session on "Machine Learning for Wireless Systems", Oct. 2018.

DONUT: CTC-based Query-by-Example Keyword Spotting

NeurIPS IRASL Workshop, 2018

Train your device to wake up for any phrase you want by recording the phrase three times, estimating the label sequence using a beam search, and computing the log probability of the label sequence at test time using the forward algorithm.

Recommended citation: L. Lugosch, S. Myer, and V. S. Tomar, “DONUT: CTC-based query-by-example keyword spotting”, NeurIPS Workshop on Interpretability and Robustness in Audio, Speech, and Language, Montreal, Canada, December 2018.