  Modern compression algorithms are often the result of laborious
domain-specific research; industry standards such as MP3, JPEG, and AMR-WB took
years to develop and were largely hand-designed. We present a deep neural
network model which optimizes all the steps of a wideband speech coding
pipeline (compression, quantization, entropy coding, and decompression)
end-to-end directly from raw speech data -- no manual feature engineering
necessary, and it trains in hours. In testing, our DNN-based coder performs on
par with the AMR-WB standard at a variety of bitrates (~9kbps up to ~24kbps).
It also runs in realtime on a 3.8GhZ Intel CPU.
