The Improvement of Wavelet Packet for Psychoacoustic Model

2012 ◽  
Vol 591-593 ◽  
pp. 2491-2494
Author(s):  
Jing Lei Zhou ◽  
Ying Li
Keyword(s):  
Spectrum Analysis ◽  
Wavelet Packet ◽  
Critical Band ◽  
Frequency Resolution ◽  
Wavelet Packet Decomposition ◽  
Frequency Division ◽  
Time Frequency ◽  
Psychoacoustic Model ◽  
Insufficient Time ◽  
The Way

Compared with common psychoacoustic model, this article uses wavelet packet decomposition to decompose a signal. This method improves the situation of insufficient time-frequency resolution which the uniform spectrum analysis causes. In addition, frequency division by wavelet packet decomposition is much closer to human’s critical band than the way common psychoacoustic model getting, it is more suitable to human’s hearing characteristics. So we can use wavelet packet decomposition replace FFT in MPEG, and get accurate psychoacoustic model.

Intelligent Identification Based on Wavelet Packet Decomposition and Adaptive Particle Swarm Optimization SVM

2013 ◽  
Vol 380-384 ◽  
pp. 4043-4046
Author(s):  
Qiang Wang ◽  
Li Jing Ren
Keyword(s):  
Support Vector Machine ◽  
Flow Regime ◽  
Wavelet Packet ◽  
Particle Swarm ◽  
Flow Regimes ◽  
Frequency Resolution ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Horizontal Pipe ◽  
Time Frequency

In this paper, a new Intelligent Identification method based on wavelet packet decomposition and APSO-SVM was put forward. As is known the characteristic of pressure drop is nonlinear and non-stationary. The wavelet packet transform can decompose signals to different frequency bands according to any time frequency resolution ratio, the features are extracted from the differential pressure fluctuation signals of the air-water two-phase flow in the horizontal pipe and the wavelet packet energy features of various flow regimes are obtained. The adaptive particle swarm ptimization support vector machine was trained using these eigenvectors as flow regime samples, and the flow regime intelligent identification was realized. The test results show the wavelet packet energy features can excellently reflect the difference between four typical flow regimes, and successful training the support vector machine can quickly and accurately identify four typical flow regimes. So a new way to identify flow regime by soft sensing is proposed.

scholarly journals Vehicle speed measurement by on-board acoustic signal processing

2018 ◽  
Vol 51 (5-6) ◽  
pp. 138-149 ◽  
Author(s):  
Hüseyin Göksu
Keyword(s):  
Relative Motion ◽  
Wavelet Packet ◽  
Absolute Error ◽  
Frequency Resolution ◽  
Vehicle Speed ◽  
Air Conditioner ◽  
Wavelet Packet Analysis ◽  
Time Frequency ◽  
Speed Sensor ◽  
Frequency Domain Methods

Estimation of vehicle speed by analysis of drive-by noise is a known technique. The methods used in this kind of practice generally estimate the velocity of the vehicle with respect to the microphone(s), so they rely on the relative motion of the vehicle to the microphone(s). There are also other methods that do not rely on this technique. For example, recent research has shown that there is a statistical correlation between vehicle speed and drive-by noise emissions spectra. This does not rely on the relative motion of the vehicle with respect to the microphone(s) so it inspires us to consider the possibility of predicting velocity of the vehicle using an on-board microphone. This has the potential for the development of a new kind of speed sensor. For this purpose we record sound signal from a vehicle under speed variation using an on-board microphone. Sound emissions from a vehicle are very complex, which is from the engine, the exhaust, the air conditioner, other mechanical parts, tires, and air resistance. These emissions carry both stationary and non-stationary information. We propose to make the analysis by wavelet packet analysis, rather than traditional time or frequency domain methods. Wavelet packet analysis, by providing arbitrary time-frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than Wavelet analysis. Subsignals from the wavelet packet analysis are analyzed further by Norm Entropy, Log Energy Entropy, and Energy. These features are evaluated by feeding them into a multilayer perceptron. Norm entropy achieves the best prediction with 97.89% average accuracy with 1.11 km/h mean absolute error which corresponds to 2.11% relative error. Time sensitivity is ±0.453 s and is open to improvement by varying the window width. The results indicate that, with further tests at other speed ranges, with other vehicles and under dynamic conditions, this method can be extended to the design of a new kind of vehicle speed sensor.

scholarly journals Time–Frequency Domain Characteristics of Acoustic Emission Signals and Critical Fracture Precursor Signals in the Deep Granite Deformation Process

2021 ◽  
Vol 11 (17) ◽  
pp. 8236
Author(s):  
Le Zhang ◽  
Hongguang Ji ◽  
Liyuan Liu ◽  
Jiwei Zhao
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
High Frequency ◽  
Wavelet Packet ◽  
Confining Pressure ◽  
Wavelet Packet Decomposition ◽  
Evolution Law ◽  
Time Frequency ◽  
Characteristic Signal ◽  
Failure Precursor

To study the crack evolution law and failure precursory characteristics of deep granite rocks in the process of deformation and failure under high confining pressure, granite samples obtained from a depth of 1150 m are tested using a TAW-2000 triaxial hydraulic servo testing machine and a PCI-II acoustic emission monitoring system. Based on the stress–strain curve and IET function, the loading process of the sample is divided into five stages: crack closure, linear elastic deformation, microcrack generation and development, macroscopic fracture generation and energy surge, and post-peak failure. The evolution trend and fracture evolution law of the acoustic emission signal event interval function in different stages are analyzed. In particular, the signals with an amplitude greater than 85 dB, a peak frequency greater than 350 kHz, and a frequency centroid greater than 275 kHz are defined as the failure precursor signals before the rock reaches the peak stress. The defined precursor signal conditions agree well with the experimental results. The time–frequency analysis and wavelet packet decomposition of the precursor signal are performed on the extracted characteristic signal of the failure precursor. The results show that the time-domain signal is in the form of a continuous waveform, and the frequency-domain waveform has multi-peak coexistence that is mainly concentrated in the high-frequency region. The energy distribution obtained by the wavelet packet decomposition of the characteristic signal is verified with the frequency-domain waveform. The energy distribution of the signal is mainly concentrated in the 343.75–375 kHz frequency band, followed by the 281.25–312.5 kHz frequency band. The energy proportion of the high-frequency signal increases with the confining pressure.

scholarly journals Flow Measurement by Wavelet Packet Analysis of Sound Emissions

2018 ◽  
Vol 51 (3-4) ◽  
pp. 104-112
Author(s):  
Hüseyin Göksu
Keyword(s):  
Flow Measurement ◽  
Acoustic Analysis ◽  
Wavelet Packet ◽  
Flow Speed ◽  
Frequency Resolution ◽  
Wavelet Packet Analysis ◽  
Resonance Effects ◽  
Time Frequency ◽  
Feature Vectors ◽  
Frequency Domain Methods

Fluid, when running through pipes, makes a complex sound emission whose parameters change nonlinearly with respect to flow speed. Especially, in household pipe systems, there may be spraying effects and resonance effects which make the emission more complex. We present a novel approach for predicting flow speed based on wavelet packet analysis of sound emissions rather than traditional time and frequency domain methods. Wavelet packet analysis, by providing arbitrary time–frequency resolution, enables analyzing signals of stationary and non-stationary nature. It has better time representation than Fourier analysis and better high-frequency resolution than wavelet analysis. Wavelet packet analysis subimages are further analyzed to obtain feature vectors of norm entropy. These feature vectors are fed into a multilayer perceptron for prediction. Prediction accuracy of 98.62%, with 3.99E−04 L/s mean absolute error and its corresponding 1.85% relative error is achieved. Time sensitivity is ±0.453 s and is open to improvement by varying window width. The result indicates that the proposed method is a good candidate for flow measurement by acoustic analysis.

MECHANICAL WATCH SIGNATURE ANALYSIS BASED ON WAVELET DECOMPOSITION

2009 ◽  
Vol 07 (04) ◽  
pp. 491-512 ◽  
Author(s):  
QINGBO HE ◽  
RUXU DU
Keyword(s):  
Acoustic Signal ◽  
Wavelet Decomposition ◽  
Wavelet Packet ◽  
Wide Frequency Range ◽  
Frequency Resolution ◽  
Signature Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Frequency Components ◽  
Wavelet Techniques

The acoustic signal of mechanical watch is a distinct multi-component signal. It contains many frequency components corresponding to specific escapement motion sources with a very wide frequency range. Therefore, it is challenging for signature analysis of mechanical watch by the acoustic signal. This paper studies the time-frequency signatures of the mechanical watch based on wavelet decomposition. Two methods are proposed to improve the frequency resolution of traditional wavelet techniques by combining other beneficial techniques in the sense of decomposing specific mono- or independent components. The empirical mode decomposition (EMD) is presented to advance the wavelet packet decomposition (WPD) to decompose the mono-component signals. And the blind source separation (BSS) makes the redundancy of continuous wavelet transform (CWT) further gain good frequency resolution in the independent meaning. The decomposed signals by the two methods reveal the insight of mechanical watch movement and can contribute much simpler and clearer time-frequency signatures. Experimental results indicated the effectiveness of the two methods and the value of the time-frequency signatures in analyzing and diagnosing mechanical watch movements.

FEATURE SELECTION FOR OPTIMIZATION OF WAVELET PACKET DECOMPOSITION IN RELIABILITY ANALYSIS OF SYSTEMS

2013 ◽  
Vol 22 (05) ◽  
pp. 1360011 ◽  
Author(s):  
RANDALL WALD ◽  
TAGHI M. KHOSHGOFTAAR ◽  
JOHN C. SLOAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Machine Learning Algorithms ◽  
Wavelet Packet Decomposition ◽  
Time Frequency ◽  
Speed Up ◽  
Frequency Domain Techniques ◽  
Feature Selection Techniques

One of the most important types of signal found in the area of machine condition monitoring/prognostic health monitoring (MCM/PHM) is the vibration signal, a type of waveform. Many time-frequency domain techniques have been proposed to interpret such signals, including wavelet packet decomposition (WPD). Previous work has shown how to extend the WPD algorithm to operate on streaming signals, but the number of output variables becomes exponential in the number of levels of decomposition, hindering data mining in limited-memory environments. Feature selection techniques, well understood in other areas of data mining, can be used to greatly reduce the number of output variables and speed up the machine learning algorithms. This paper presents a case study comparing two versions of WPD both with and without feature selection, demonstrating that removing most of the features produced by the WPD does not impair its performance within the context of MCM/PHM.

Time-Frequency Characteristics of Vibration Signals Analysis for Large Instantaneous Impact Mill Rolling Based on Wavelet Packet

2013 ◽  
Vol 834-836 ◽  
pp. 1061-1064
Author(s):  
Qi Jun Xiao ◽  
Zhong Hui Luo
Keyword(s):  
Time Domain ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Signal Wave ◽  
Reconstruction Technique ◽  
Wavelet Packet Decomposition ◽  
Vibration Signals ◽  
Time Frequency ◽  
Impact Vibration ◽  
Signal Time

The wavelet packet decomposition and reconstruction technique is applied to time-frequency analysis of bite steel impact vibration signal by big rolling machine, it is obtained the bite steel impact signal wave packet. According to the size of the wavelet packet energy, it is reconstructed the signal of No.1 and No.2 wavelet packet. According to reconstruction of the signal time domain waveform and FFT spectrum chart, some meaningful conclusions are obtained.

scholarly journals Efficiently Synchronized Spread-Spectrum Audio Watermarking with Improved Psychoacoustic Model

2008 ◽  
Vol 2008 ◽  
pp. 1-5 ◽  
Author(s):  
Xing He ◽  
Michael S. Scordilis
Keyword(s):  
Spread Spectrum ◽  
Wavelet Packet ◽  
Audio Watermarking ◽  
Discrete Wavelet ◽  
Digital Audio ◽  
Discrete Wavelet Packet Transform ◽  
Time Frequency ◽  
Psychoacoustic Model ◽  
Temporal Masking ◽  
Watermarking Scheme

This paper presents an audio watermarking scheme which is based on an efficiently synchronized spread-spectrum technique and a new psychoacoustic model computed using the discrete wavelet packet transform. The psychoacoustic model takes advantage of the multiresolution analysis of a wavelet transform, which closely approximates the standard critical band partition. The goal of this model is to include an accurate time-frequency analysis and to calculate both the frequency and temporal masking thresholds directly in the wavelet domain. Experimental results show that this watermarking scheme can successfully embed watermarks into digital audio without introducing audible distortion. Several common watermark attacks were applied and the results indicate that the method is very robust to those attacks.

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