Time domain information from resonant Raman excitation profiles: A direct inversion by maximum entropy

1993 ◽  
Vol 99 (7) ◽  
pp. 4908-4925 ◽  
Author(s):  
F. Remacle ◽  
R. D. Levine
Keyword(s):  
Maximum Entropy ◽  
Time Domain ◽  
Direct Inversion ◽  
Resonant Raman ◽  
Raman Excitation Profiles ◽  
Domain Information

Vibronic coupling in nickel(II) porphine derived from resonant Raman excitation profiles

1993 ◽  
Vol 97 (39) ◽  
pp. 9956-9968 ◽  
Author(s):  
E. Unger ◽  
U. Bobinger ◽  
W. Dreybrodt ◽  
R. Schweitzer-Stenner
Keyword(s):  
Vibronic Coupling ◽  
Resonant Raman ◽  
Raman Excitation Profiles

scholarly journals Determination of the time origin by 
the maximum entropy method in 
time-domain terahertz emission spectroscopy

2010 ◽  
Vol 18 (15) ◽  
pp. 15853 ◽  
Author(s):  
Takeya Unuma ◽  
Yusuke Ino ◽  
Makoto Kuwata-Gonokami ◽  
Erik M. Vartiainen ◽  
Kai-Erik Peiponen ◽  
...  
Keyword(s):  
Maximum Entropy ◽  
Time Domain ◽  
Emission Spectroscopy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Terahertz Emission ◽  
Terahertz Emission Spectroscopy ◽  
Time Origin

ChemInform Abstract: Continuously Scanned Resonant Raman Excitation Profiles for Iodobenzene Excited in the B Continuum.

ChemInform ◽  
2010 ◽  
Vol 24 (26) ◽  
pp. no-no
Author(s):  
C.-Y. KUNG ◽  
B.-Y. CHANG ◽  
C. KITTRELL ◽  
B. R. JOHNSON ◽  
J. L. KINSEY
Keyword(s):  
Resonant Raman ◽  
Raman Excitation Profiles

scholarly journals Tone Recognition Database of Electronic Pipe Organ Based on Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuyi Zhao
Keyword(s):  
Artificial Intelligence ◽  
Fourier Transform ◽  
Human Computer Interaction ◽  
Time Domain ◽  
Pipe Organ ◽  
Interaction Interface ◽  
Tone Recognition ◽  
Audio Data ◽  
The Time Domain ◽  
Domain Information

In the past few decades, artificial intelligence technology has experienced rapid development, and its application in modern industrial systems has grown rapidly. This research mainly discusses the construction of a database of electronic pipe organ tone recognition based on artificial intelligence. The timbre synthesis module realizes the timbre synthesis of the electronic pipe organ according to the current timbre parameters. The audio time domain information (that is, the audio data obtained by file analysis) is framed and windowed, and fast Fourier transform (FFT) is performed on each frame to obtain the frequency domain information of each frame. The harmonic peak method based on improved confidence is used to identify the pitch, obtain the fundamental tone of the tone, and calculate its multiplier. Based on the timbre parameters obtained in the timbre parameter editing interface, calculate the frequency domain information of the synthesized timbre of each frame, and then perform the inverse Fourier transform to obtain the time domain waveform of each frame; connect the time domain waveforms of different frames by the cross-average method to obtain the time-domain waveform of the synthesized tone (that is, the audio data of the synthesized tone). After collecting the sound of the electronic pipe organ, the audio needs to be denoised, and the imported audio file needs to be parsed to obtain the audio data information. Then, the audio data are frequency-converted and the timbre characteristic information is analyzed; the timbre parameters are obtained through the human-computer interaction interface based on artificial intelligence, and the timbre of the electronic pipe organ is generated. If the timbre effect is not satisfactory, you can re-edit the timbre parameters through the human-computer interaction interface to generate timbre. During the experiment, the overall recognition rate of 3762 notes and 286 beats was 88.6%. The model designed in this study can flexibly generate electronic pipe organ sound libraries of different qualities to meet the requirements of sound authenticity.

scholarly journals An Application of Time-Frequency-Based Analysis Method for Identifying Bolt Anchorage System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
H. Q. Zheng ◽  
Y. Zhang ◽  
G. Han ◽  
X. Y. Sun
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Identification Accuracy ◽  
Analysis Method ◽  
Defect Identification ◽  
Time Frequency ◽  
Reinforcing Bar ◽  
Anchorage System ◽  
Convolution Kernels ◽  
Domain Information

A rock bolt refers to a reinforcing bar used commonly in geotechnical engineering. Also, defect identification of bolt anchorage system determines the safe operation of the reinforced structures. In the present paper, to accurately extract defect information, a CNN model based on time-frequency analysis is proposed, covering both time-domain and frequency-domain information. The effect of the number of convolution kernels on the defect identification results is discussed. By laboratory experiments, the performances of STFT-based CNN with those of time-domain input or frequency-domain input-based 1D CNN are compared, and the results demonstrate that the proposed method showed enhanced performance in identification accuracy.

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