Speech Coding Using Discrete Cosine Transform and Chaotic Map

Abstract

Recently, data of multimedia performs an exponentially blowing tendency, saturating daily life of humans. Various modalities of data, includes images, texts and video, plays important role in different aspects and has wide. However, the key problem of utilizing data of large scale is cost of processing and massive storage. Therefore, for efficient communications and for economical storage requires effective techniques of data compression to reduce the volume of data. Speech coding is a main problem in the area of digital speech processing. The process of converting the voice signals into a more compressed form is speech coding. In this work, we demonstrate that a DCT with a chaotic system combined with run-length coding can be utilized to implement speech coding of very low bit-rate with high reconstruction quality. Experimental result show that compression ratio is about 13% when implemented on Librispeech dataset.

Facial Emotion Recognition Using HOG and Convolution Neural Network

“…This executive and attentional control is intimately related to the process of learning, as intrinsically limited attentional abilities are better concentrated on relevant information. Emotion also aids in the information retrieval and efficient encoding [2,3].…”