DeepAI AI Chat
Log In Sign Up

Multi-layered Cepstrum for Instantaneous Frequency Estimation

by   Chin-Yun Yu, et al.

We propose the multi-layered cepstrum (MLC) method to estimate multiple fundamental frequencies (MF0) of a signal under challenging contamination such as high-pass filter noise. Taking the operation of cepstrum (i.e., Fourier transform, filtering, and nonlinear activation) recursively, MLC is shown as an efficient method to enhance MF0 saliency in a step-by-step manner. Evaluation on a real-world polyphonic music dataset under both normal and low-fidelity conditions demonstrates the potential of MLC.


Fast Nonlinear Fourier Transform using Chebyshev Polynomials

We explore the class of exponential integrators known as exponential tim...

Neural Architectures Learning Fourier Transforms, Signal Processing and Much More....

This report will explore and answer fundamental questions about taking F...

Efficient Nonlinear Fourier Transform Algorithms of Order Four on Equispaced Grid

We explore two classes of exponential integrators in this letter to desi...

TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder

Texture is an essential information in image representation, capturing p...

Fundamental component enhancement via adaptive nonlinear activation functions

In many real world oscillatory signals, the fundamental component of a s...

Decision-Feedback Detection Strategy for Nonlinear Frequency-Division Multiplexing

By exploiting a causality property of the nonlinear Fourier transform, a...