# Significance of Chirp MFCC as a Feature in Speech and Audio Applications

arXiv: https://arxiv.org/abs/2402.12239 · HTML: https://mlsanity.com/paper/2402.12239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Quartered Chirp Spectral Envelope for Whispered vs Normal Speech Classification](https://mlsanity.com/paper/2408.14777/) (2024)

## Abstract (arXiv)

A novel feature, based on the chirp z-transform, that offers an improved representation of the underlying true spectrum is proposed. This feature, the chirp MFCC, is derived by computing the Mel frequency cepstral coefficients from the chirp magnitude spectrum, instead of the Fourier transform magnitude spectrum. The theoretical foundations for the proposal, and the experimental validation using product of likelihood Gaussians, to show the improved class separation offered by the proposed chirp MFCC, when compared with vanilla MFCC are discussed. Further, real world evaluation of the feature is performed using three diverse tasks, namely, speech-music classification, speaker identification, and speech commands recognition. It is shown in all three tasks that the proposed chirp MFCC offers considerable improvements.