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Fig 1 is an example of a SCCC. Fig. 1. SCCC Encoder. The example encoder is composed of a 16-state outer convolutional code and a 2-state inner convolutional code linked by an interleaver. The natural code rate of the configuration shown is 1/4, however, the inner and/or outer codes may be punctured to achieve higher code rates as needed.
A convolutional encoder is a discrete linear time-invariant system. Every output of an encoder can be described by its own transfer function, which is closely related to the generator polynomial. An impulse response is connected with a transfer function through Z-transform. Transfer functions for the first (non-recursive) encoder are:
Code-excited linear prediction (CELP) is a linear predictive speech coding algorithm originally proposed by Manfred R. Schroeder and Bishnu S. Atal in 1985. At the time, it provided significantly better quality than existing low bit-rate algorithms, such as residual-excited linear prediction (RELP) and linear predictive coding (LPC) vocoders (e.g., FS-1015).
One encoder-decoder block A Transformer is composed of stacked encoder layers and decoder layers. Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process all the input tokens together one layer after another, while the decoder consists of decoding ...
During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text.
High-level schematic diagram of BERT. It takes in a text, tokenizes it into a sequence of tokens, add in optional special tokens, and apply a Transformer encoder. The hidden states of the last layer can then be used as contextual word embeddings. BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of 4 modules:
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...
Linear predictive coding (LPC) is a method used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive model.