ORCIDGitHubLinkedIn
RSS Curriculum Vitae TR

Discrete Cosine Transform

Turkish equivalent: Ayrık kosinüs dönüşümüDomain: Signal Processing

A transform that represents a finite signal as weighted cosine basis functions and is widely used for energy compaction in compression and feature extraction.

Signal Processing Context

The Discrete Cosine Transform represents a finite sequence with cosine basis functions and concentrates correlated signal energy into a relatively small number of coefficients. Speech processing commonly applies a DCT to log Mel-filter-bank energies when forming MFCC features.

Measurement Boundary

The DCT is not interchangeable with the DFT: it uses real cosine bases and different boundary assumptions. Transform normalization also varies between implementations and must be fixed when coefficients are compared.

Updated:

Related concepts

Signal Processing

Related technical publications

Publications whose title or summary directly references this concept.

Speech Feature Extraction with MFCC

MFCC reduces a short-time speech spectrum to a compact coefficient vector through Mel scaling, logarithmic energy, and the DCT; framing and filter-bank choices directly affect feature stability.