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PLC instructions

--- a/dnn/README.md

+++ b/dnn/README.md

@@ -5,7 +5,13 @@

 - J.-M. Valin, J. Skoglund, [LPCNet: Improving Neural Speech Synthesis Through Linear Prediction](https://jmvalin.ca/papers/lpcnet_icassp2019.pdf), *Proc. International Conference on Acoustics, Speech and Signal Processing (ICASSP)*, arXiv:1810.11846, 2019.

 - J.-M. Valin, J. Skoglund, [A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet](https://jmvalin.ca/papers/lpcnet_codec.pdf), *Proc. INTERSPEECH*, arxiv:1903.12087, 2019.

 - J. Skoglund, J.-M. Valin, [Improving Opus Low Bit Rate Quality with Neural Speech Synthesis](https://jmvalin.ca/papers/opusnet.pdf), *Proc. INTERSPEECH*, arxiv:1905.04628, 2020.

+- J.-M. Valin, U. Isik, P. Smaragdis, A. Krishnaswamy, [Neural Speech Synthesis on a Shoestring: Improving the Efficiency of LPCNet](https://jmvalin.ca/papers/improved_lpcnet.pdf), *Proc. ICASSP*, arxiv:2106.04129, 2022.

+- K. Subramani, J.-M. Valin, U. Isik, P. Smaragdis, A. Krishnaswamy, [End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation](https://jmvalin.ca/papers/lpcnet_end2end.pdf), arxiv:2106.04129, 2022.

+with the PLC algorithm described in:

+

+- J.-M. Valin, A. Mustafa, C. Montgomery, T.B. Terriberry, M. Klingbeil, P. Smaragdis, A. Krishnaswamy, [Real-Time Packet Loss Concealment With Mixed Generative and Predictive Model](https://jmvalin.ca/papers/lpcnet_plc.pdf), *Submitted to INTERSPEECH 2022*, arxiv:2205.05785.

+

 # Introduction

 Work in progress software for researching low CPU complexity algorithms for speech synthesis and compression by applying Linear Prediction techniques to WaveRNN. High quality speech can be synthesised on regular CPUs (around 3 GFLOP) with SIMD support (SSE2, SSSE3, AVX, AVX2/FMA, NEON currently supported). The code also supports very low bitrate compression at 1.6 kb/s.

@@ -58,6 +64,15 @@

 Alternatively, you can run the uncompressed analysis/synthesis using -features

 instead of -encode and -synthesis instead of -decode.

 The same functionality is available in the form of a library. See include/lpcnet.h for the API.

+

+Packet loss concealment can be tested with:

+```

+./lpcnet_demo -plc_file noncausal_dc error_pattern.txt input.pcm output.pcm

+```

+

+where error_pattern.txt is a text file with one entry per 20-ms packet, with 1 meaning "packet lost" and 0 meaning "packet not lost".

+noncausal_dc is the non-causal (5-ms look-ahead) with special handling for DC offsets. It's also possible to use "noncausal", "causal",

+or "causal_dc".

 # Training a new model

-- 

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