scholarly journals A design of laser array harp based on multi-dimensional wavelet transform and audio signal reconstruction

2021 ◽  
Vol 2113 (1) ◽  
pp. 012059
Author(s):  
Bin Liu ◽  
Yan Ren
Keyword(s):  
Wavelet Transform ◽  
Analog Circuits ◽  
Signal Reconstruction ◽  
Green Light ◽  
Processing System ◽  
Audio Signal ◽  
Laser Sensor ◽  
Laser Array ◽  
Audio Processing ◽  
Control Command

Abstract This paper introduces a design scheme of laser array harp based on multi-dimensional wavelet transform and audio signal reconstruction. The green light beams from multiple high-power lasers simulate harp strings, use photoresistors as the signal receiving end, and use a signal conditioning system composed of analog circuits and LM393 comparators to collect and adjust the resistance signal of the laser sensor[1], and finally it is adjusted to a level signal that can be recognized by the CPU. After receiving the signal, the CPU core board analyzes the string signal, and sends control commands to the audio processing system through the industrial bus according to the analyzed digital signal. After receiving the control command, the audio processing system uses the audio signal reconstruction technology composed of multi-dimensional wavelet packets, deep learning and other algorithms to simulate the audio signals of various string music, so as to achieve the purposes of using the lasers as virtual strings and imitating musical instruments for musical performance.[2]

A Guide on Using Xilinx System Generator to Design and Implement Real-Time Audio Effects on FPGA

2021 ◽  
pp. 38-44
Author(s):  
L. Merah ◽  
◽  
P. Lorenz ◽  
A. Ali-Pacha ◽  
N. Hadj-Said ◽  
...  
Keyword(s):  
Real Time ◽  
Power Efficiency ◽  
Audio Signal ◽  
Research Area ◽  
Audio Processing ◽  
The Real ◽  
System Generator ◽  
Xilinx System Generator ◽  
Active Research ◽  
Implementation Steps

The enormous progress in communication technology has led to a tremendous need to provide an ideal environment for the transmission, storing, and processing of digital multimedia content, where the audio signal takes the lion's share of it. Audio processing covers many diverse fields, its main aim is presenting sound to human listeners. Recently, digital audio processing became an active research area, it covers everything from theory to practice in relation to transmission, compression, filtering, and adding special effects to an audio signal. The aim of this work is to present the real-time implementation steps of some audio effects namely, the echo and Flanger effects on Field Programmable Gate Array (FPGA). Today, FPGAs are the best choice in data processing because they provide more flexibility, performance, and huge processing capabilities with great power efficiency. Designs are achieved using the XSG tool (Xilinx System Generator), which makes complex designs easier without prior knowledge of hardware description languages. The paper is presented as a guide with deep technical details about designing and real-time implementation steps. We decided to transfer some experience to designers who want to rapidly prototype their ideas using tools such as XSG. All the designs have been simulated and verified under Simulink/Matlab environment, then exported to Xilinx ISE (Integrated Synthesis Environment) tool for the rest of the implementation steps. The paper also gives an idea of interfacing the FPGA with the LM4550 AC’97 codec using VHDL coding. The ATLYS development board based on Xilinx Spartan-6 LX45 FPGA is used for the real-time implementation.

scholarly journals ANALYSIS AND IMPROVISATION IN EXTRACTING AUDIO SIGNAL AMPLITUDE USING LABVIEW

2020 ◽  
pp. 273-278
Author(s):  
Adarsh V Srinivasan ◽  
Mr. N. Saritakumar
Keyword(s):  
Virtual Instrument ◽  
Sampling Rate ◽  
Audio Signal ◽  
Signal Amplitude ◽  
Audio Signals ◽  
Virtual Instrumentation ◽  
Audio Processing ◽  
Reading And Writing ◽  
Labview Software ◽  
Frequency Sampling

In this paper, either a pre-recorded audio or a newly recorded audio is processed and analysed using the LabVIEW Software by National Instruments. All the data such as bitrate, number of channels, frequency, sampling rate of the Audio are analyzed and improvising the signal by a few operations like Amplification, De-Amplification, Inversion and Interlacing of Audio Signals are done. In LabVIEW, there are a few Sub Virtual Instrument’s available for Reading and Writing Audio in .wav formats and using them and array Sub Virtual Instrument, all the processing are done. KEYWORDS: Virtual Instrumentation (VI), LabVIEW (LV), Audio, Processing, audio array.

scholarly journals LSTM with Wavelet Transform Based Data Preprocessing for Stock Price Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaodan Liang ◽  
Zhaodi Ge ◽  
Liling Sun ◽  
Maowei He ◽  
Hanning Chen
Keyword(s):  
Wavelet Transform ◽  
Stock Price ◽  
Signal Reconstruction ◽  
Data Preprocessing ◽  
Original Data ◽  
Training Data ◽  
Threshold Method ◽  
Denoising Method ◽  
Stock Price Prediction ◽  
Price Prediction

For profit maximization, the model-based stock price prediction can give valuable guidance to the investors. However, due to the existence of the high noise in financial data, it is inevitable that the deep neural networks trained by the original data fail to accurately predict the stock price. To address the problem, the wavelet threshold-denoising method, which has been widely applied in signal denoising, is adopted to preprocess the training data. The data preprocessing with the soft/hard threshold method can obviously restrain noise, and a new multioptimal combination wavelet transform (MOCWT) method is proposed. In this method, a novel threshold-denoising function is presented to reduce the degree of distortion in signal reconstruction. The experimental results clearly showed that the proposed MOCWT outperforms the traditional methods in the term of prediction accuracy.

AUDIO WATERMARKING WITH ANGLE QUANTIZATION BASED ON DISCRETE WAVELET TRANSFORM AND SINGULAR VALUE DECOMPOSITION

2018 ◽  
Vol 6 (2) ◽  
pp. 41-52
Author(s):  
Divya Chadar ◽  
Shailja Shukla
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Auditory System ◽  
Audio Signal ◽  
Singular Value ◽  
New Method ◽  
Discrete Wavelet ◽  
Audio Watermark ◽  
Value Decomposition

An audio watermark is a unique electronic identifier embedded in an audio signal, typically used to identify ownership of copyright. Proposed work is a new method of audio watermark hiding inside another bigger cover standard audio cover. The method includes ‘harr’ wavelet based Discrete Wavelet Transform decomposition of frequencies hence the audio samples of watermark gets hidden only those parts of cover audio where human ears are less sensible according to Human Auditory System. Proposed method also includes the Singular Value Decomposition, which is required for making our method robust against the various communication of processing attacks like compression, filtering, fading or noise addition. Proposed work is also using the concept of angular modulation which initially modifies the audio watermark in to provide extra security and also extra robustness in communication. The design is been develop on MATLAB 2013b version and verification of design o the same. 

Brain Computer Interface for Communication and Control of Peripherals and Appliances

2020 ◽  
Vol 17 (4) ◽  
pp. 1616-1621
Author(s):  
K. Sathish ◽  
Aritra Paul ◽  
Debapriya Roy ◽  
Ishmeet Kalra ◽  
Simran Bajaj
Keyword(s):  
Brain Computer Interface ◽  
Processing System ◽  
Audio Signal ◽  
Computer Interface ◽  
Single Frequency ◽  
Discrete Wavelet ◽  
Continuous Eeg ◽  
Transmission Reduction ◽  
The Subject ◽  
Simplified Solution

The concept is designed to improve upon the recent developed system, utilizing auditory steady state response (ASSR) as a basis for the Brain Computer Interface (BCI) paradigm. It utilizes the classification of signals through a discrete wavelet transform (DWT) before the actual transmission to reduce overhead at the processing system. The electroencephalogram (EEG) obtained from the subject is through a p300 based EEG receivers. A compression algorithm is used to reduce the bandwidth usage and provide a quicker transmission of the large and continuous EEG. An Arduino board along with a proximity sensor is used to detect the presence and distance of the subject and consequently control playback of a single frequency audio signal, which as received by the user, is used for producing the EEG signals. A continuous focus of the user is required on the playback of the single frequency sound to produce a sizeable reading. At the receiving end, another Arduino board is installed with an SD card module, which contains the commands, responsible for the actual control of the devices. The concept can be utilized for various purposes from controlling IoT based systems to wheelchairs and hospital beds as well as bionic limbs, which however are limited due to the overall bulk of all the equipment currently required. The main aim of this paper is to propose an improvement in the transmission, reduction the latency of the signals and to provide a concept for utilization by the handicapped or physically impaired patients. Since the EEG is obtained through the inner ear of the subject, it completely eliminates any need for invasive surgery and provides a simplified solution. Developments have shown to be able to achieve over 95% of accuracy in the domain, currently limited by length of the EEG required in order to process the actual commands from the subject’s brain.

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