Time-frequency error measures for dynamic signal reproduction

1998 ◽  
Vol 38 (4) ◽  
pp. 303-305
Author(s):  
R. M. French
Keyword(s):  
Frequency Error ◽  
Time Frequency ◽  
Error Measures ◽  
Dynamic Signal

The Time-Frequency Error Product in Time-Resolved Spectroscopy

1976 ◽  
Vol 15 (4) ◽  
Author(s):  
H. J. Caulfield ◽  
T. B. Hirschfeld ◽  
A. D. Williams
Keyword(s):  
Frequency Error ◽  
Time Resolved ◽  
Time Resolved Spectroscopy ◽  
Time Frequency

Influence of input data representations for time-dependent instrument recognition

2021 ◽  
Vol 88 (5) ◽  
pp. 274-281
Author(s):  
Markus Schwabe ◽  
Michael Heizmann
Keyword(s):  
Signal Processing ◽  
Three Dimensional ◽  
Time Dependent ◽  
Frequency Error ◽  
Additional Time ◽  
Time Frequency ◽  
Music Signal ◽  
Frequency Representation ◽  
Instrument Recognition ◽  
Signal Processing Algorithms

Abstract An important preprocessing step for several music signal processing algorithms is the estimation of playing instruments in music recordings. To this aim, time-dependent instrument recognition is realized by a neural network with residual blocks in this approach. Since music signal processing tasks use diverse time-frequency representations as input matrices, the influence of different input representations for instrument recognition is analyzed in this work. Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based on phase information are investigated as well as two-dimensional STFT or constant-Q transform (CQT) magnitudes. As additional phase representations, the product spectrum (PS), based on the modified group delay, and the frequency error (FE) matrix, related to the instantaneous frequency, are used. Training and evaluation processes are executed based on the MusicNet dataset, which enables the estimation of seven instruments. With a higher number of frequency bins in the input representations, an improved instrument recognition of about 2 % in F1-score can be achieved. Compared to the literature, frame-level instrument recognition can be improved for different input representations.

Time‐frequency representations of auditory signatures: Dynamic signal models

1992 ◽  
Vol 92 (4) ◽  
pp. 2362-2362
Author(s):  
Shelley E. Scruggs ◽  
Gregory H. Wakefield
Keyword(s):  
Time Frequency ◽  
Dynamic Signal ◽  
Signal Models

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

Time-Frequency Analysis of Partial Discharge Phenomena in SF6 gas using Wavelet Transform

1997 ◽  
Vol 117 (3) ◽  
pp. 338-345 ◽  
Author(s):  
Masatake Kawada ◽  
Masakazu Wada ◽  
Zen-Ichiro Kawasaki ◽  
Kenji Matsu-ura ◽  
Makoto Kawasaki
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Partial Discharge ◽  
Time Frequency Analysis ◽  
Time Frequency

Time-Frequency Channel Parameterization with Application to Multi-Mode Receivers

2009 ◽  
Vol E92-B (12) ◽  
pp. 3717-3725
Author(s):  
Thomas HUNZIKER ◽  
Ziyang JU ◽  
Dirk DAHLHAUS
Keyword(s):  
Frequency Channel ◽  
Time Frequency ◽  
Multi Mode
Sign in / Sign up

Export Citation Format

Share Document