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 A Review on Machine Learning for Audio Applications

2021 ◽  
Vol 23 (07) ◽  
pp. 62-70
Author(s):  
Nagesh B ◽  
◽  
Dr. M. Uttara Kumari ◽  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Language Processing ◽  
Speech Processing ◽  
Noise Suppression ◽  
Audio Signal ◽  
Audio Signals ◽  
Audio Processing ◽  
Speech Generation

Audio processing is an important branch under the signal processing domain. It deals with the manipulation of the audio signals to achieve a task like filtering, data compression, speech processing, noise suppression, etc. which improves the quality of the audio signal. For applications such as natural language processing, speech generation, automatic speech recognition, the conventional algorithms aren’t sufficient. There is a need for machine learning or deep learning algorithms which can be implemented so that the audio signal processing can be achieved with good results and accuracy. In this paper, a review of the various algorithms used by researchers in the past has been described and gives the appropriate algorithm that can be used for the respective applications.

Virtual instrument designed for data acquisition

2020 ◽  
Vol 65 (1) ◽  
pp. 179-186
Author(s):  
Mihaela Dorica Stroia
Keyword(s):  
Software Development ◽  
Data Acquisition ◽  
Virtual Instrument ◽  
Virtual Instrumentation ◽  
Design And Implementation ◽  
Adequate Design

Current software development directions open up a world of possibilities, especially in the engineering field. Present paper is meant to highlight the advantages and in particular the ease of using virtual instrumentation facilities, with a proper and adequate design and implementation of desired instrument. In this idea we bring into discussion a design for virtual instrument which can be used for data acquisition that can be stored for further simulations according to the needs required by the process in discussion.

scholarly journals IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 676
Author(s):  
Andrej Zgank
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Audio Signal ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Animal Activity ◽  
The Impact ◽  
Acoustic Classification ◽  
Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

scholarly journals Stochastic Restoration of Heavily Compressed Musical Audio Using Generative Adversarial Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1349
Author(s):  
Stefan Lattner ◽  
Javier Nistal
Keyword(s):  
Data Storage ◽  
Audio Signal ◽  
Human Perception ◽  
Generative Adversarial Networks ◽  
Audio Signals ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Extensive Evaluation ◽  
Listening Tests ◽  
Musical Audio

Lossy audio codecs compress (and decompress) digital audio streams by removing information that tends to be inaudible in human perception. Under high compression rates, such codecs may introduce a variety of impairments in the audio signal. Many works have tackled the problem of audio enhancement and compression artifact removal using deep-learning techniques. However, only a few works tackle the restoration of heavily compressed audio signals in the musical domain. In such a scenario, there is no unique solution for the restoration of the original signal. Therefore, in this study, we test a stochastic generator of a Generative Adversarial Network (GAN) architecture for this task. Such a stochastic generator, conditioned on highly compressed musical audio signals, could one day generate outputs indistinguishable from high-quality releases. Therefore, the present study may yield insights into more efficient musical data storage and transmission. We train stochastic and deterministic generators on MP3-compressed audio signals with 16, 32, and 64 kbit/s. We perform an extensive evaluation of the different experiments utilizing objective metrics and listening tests. We find that the models can improve the quality of the audio signals over the MP3 versions for 16 and 32 kbit/s and that the stochastic generators are capable of generating outputs that are closer to the original signals than those of the deterministic generators.

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 The Influence of Composite Polymeric Materials Topology Over the Shearing Modulus Using Virtual Instruments

2018 ◽  
Vol 55 (4) ◽  
pp. 524-530
Author(s):  
Marinela Marinescu ◽  
Larisa Butu ◽  
Claudia Borda ◽  
Delicia Arsene ◽  
Mihai Butu
Keyword(s):  
Composite Material ◽  
Composite Materials ◽  
Virtual Instrument ◽  
Mechanical Characteristics ◽  
Polymeric Materials ◽  
Mechanical Impedance ◽  
Impedance Analysis ◽  
Cross Linking ◽  
Labview Software ◽  
Different Temperatures

This study presents research regarding the calculation of the mechanical characteristics of composite polymeric materials. By using LabVIEW� software a virtual instrument was created used for monitoring in real time the process of cross-linking the composite polymeric materials. The experiments were realized based on composite materials containing epoxy/fiberglass resin of different topologies. By means of the virtual instrument and of a sensor created based on the mechanical impedance analysis, implanted in the composite material, it was determined the G shearing module of the composite material at different temperatures.

scholarly journals Layered multiple description coding of multi-channel audio signals

2021 ◽  
Author(s):  
Katarina Stojadinović
Keyword(s):  
Audio Signal ◽  
Multiple Description Coding ◽  
Packet Networks ◽  
Audio Signals ◽  
Packet Losses ◽  
Loss Mitigation ◽  
Efficient Coding ◽  
Multiple Description ◽  
Redundancy Coding ◽  
Description Coding

In this study, we investigate efficient coding of multi-channel audio signals for transmission over packet networks. The techniques studied and developed as part of this research are based on redundancy coding and aim to achieve robustness with respect to packet losses. The resulting algorithm also addresses the needs of network clients with varying access bandwidths; the algorithm generates multi-layer encoded data streams which can range from basic mono to full multi-channel surround audio. Loss mitigation is achieved by applying multiple description coding technique based on the priority encoding transmission packetization scheme. The hierarchy of the transmitted data is derived from a statistical analysis of the multi-channel audio signal. Inter-channel correlations form the basis for estimating the multi-channel audio signal form the received descriptions at the decoder.

scholarly journals Automated Test System for Test and Evaluation of C-Band Transmitter Packages for Geosat Spacecrafts

2019 ◽  
Vol 8 (2) ◽  
pp. 4591-4596
Keyword(s):  
Rapid Development ◽  
User Interaction ◽  
Test System ◽  
General Purpose ◽  
Virtual Instrumentation ◽  
Labview Software ◽  
Test And Evaluation ◽  
Automated Test System ◽  
Automated Test ◽  
Component Interface

The aim of this paper is to develop an Automated Test System (ATS) for the Test and Evaluation of C-Band Transmitter packages for GEOSAT Space crafts using Virtual Instrumentation. Efficiency, coverage, quality and accuracy for the test and evaluation of Device Under Test (DUT) can be increased by Automated Testing. Minimizing the errors anticipated with manual intervention. Automated Test System using Virtual instrumentation (VI) combines rapid development software and modular, flexible hardware to create user-defined test systems. Here Modular PXI (Peripheral component interface Extensions for Instrumentation) instruments from National Instruments are used with NI-LabVIEW software for realizing the ATS. For characterizing the C-Band Transmitter, Spectrum analyzer & Digital Multimeter (DMM) is configured in PXI form-factor and the Power supply is controlled through GPIB (General Purpose Interface Bus) bus. The complete software is developed using NI LabVIEW which takes care of configuring the test condition and analyzing the DUT performance. The user friendly GUI well takes care of user interaction to the ATS.

scholarly journals Content Based Audio Watermarking and Retrieval Using Time-Frequency Analysis

2021 ◽  
Author(s):  
Shahrzad Esmaili
Keyword(s):  
Frequency Analysis ◽  
Spread Spectrum ◽  
Data Privacy ◽  
Audio Signal ◽  
Maximum Energy ◽  
Audio Watermarking ◽  
Audio Signals ◽  
Time Frequency Analysis ◽  
Linear Discriminant ◽  
Time Frequency

This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.

scholarly journals Dynamics of the formation of a differentiation track conditioned reflex to sound stimuli of a certain duration in the gray seal Halichoerus grypus Fabricius, 1791 by the methods of operant conditioning.

2021 ◽  
Vol 12 (3-2021) ◽  
pp. 7-13
Author(s):  
A.F. Berdnik ◽  
Keyword(s):  
Reaction Time ◽  
Operant Conditioning ◽  
Conditioned Reflex ◽  
Audio Signal ◽  
Halichoerus Grypus ◽  
Audio Signals ◽  
Gray Seal ◽  
And Behavior ◽  
Sound Duration

In the course of the study, a 15-year-old female gray seal was trained to press a button after displaying an audio signal for 5 seconds and ignore similar audio signals of longer or shorter duration. The conducted research has demonstrated the ability of the experimental seal to reliably differentiate sound signals with a difference in sound duration of 3 seconds. Changes in the reaction time and behavior of the seal during the demonstration of sound stimuli with distinguishable and indistinguishable time ranges are described.

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