scholarly journals Simultaneous Audio Encryption and Compression Using Compressive Sensing Techniques

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 863 ◽  
Author(s):  
Rodolfo Moreno-Alvarado ◽  
Eduardo Rivera-Jaramillo ◽  
Mariko Nakano ◽  
Hector Perez-Meana
Keyword(s):  
Compressive Sensing ◽  
Audio Signal ◽  
Communication Channels ◽  
Digital Information ◽  
Audio Signals ◽  
Sensing Matrix ◽  
Coding Schemes ◽  
Audio Encryption ◽  
Active Research ◽  
Audio Information

The development of coding schemes with the capacity to simultaneously encrypt and compress audio signals is a subject of active research because of the increasing necessity for transmitting sensitive audio information over insecure communication channels. Thus, several schemes have been developed; firstly, some of them compress the digital information and subsequently encrypt the resulting information. These schemas efficiently compress and encrypt the information. However, they may compromise the information as it can be accessed before encryption. To overcome this problem, a compressing sensing-based system to simultaneously compress and encrypt audio signals is proposed in which the audio signal is segmented in frames of 1024 samples and transformed into a sparse frame using the discrete cosine transform (DCT). Each frame is then multiplied by a different sensing matrix generated using the chaotic mixing scheme. This fact allows that the proposed scheme satisfies the extended Wyner secrecy (EWS) criterion. The evaluation results obtained using several genres of audio signals show that the proposed system allows to simultaneously compress and encrypt audio signals, satisfying the EWS criterion.

scholarly journals Audio Encryption Algorithm Based on Chen Memristor Chaotic System

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 17
Author(s):  
Wanying Dai ◽  
Xiangliang Xu ◽  
Xiaoming Song ◽  
Guodong Li
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Chaotic System ◽  
Basis Function ◽  
Color Image ◽  
Audio Signal ◽  
Encryption Algorithm ◽  
Audio Signals ◽  
Encryption Algorithms ◽  
Audio Encryption

The data space for audio signals is large, the correlation is strong, and the traditional encryption algorithm cannot meet the needs of efficiency and safety. To solve this problem, an audio encryption algorithm based on Chen memristor chaotic system is proposed. The core idea of the algorithm is to encrypt the audio signal into the color image information. Most of the traditional audio encryption algorithms are transmitted in the form of noise, which makes it easy to attract the attention of attackers. In this paper, a special encryption method is used to obtain higher security. Firstly, the Fast Walsh–Hadamar Transform (FWHT) is used to compress and denoise the signal. Different from the Fast Fourier Transform (FFT) and the Discrete Cosine Transform (DCT), FWHT has good energy compression characteristics. In addition, compared with that of the triangular basis function of the Fast Fourier Transform, the rectangular basis function of the FWHT can be more effectively implemented in the digital circuit to transform the reconstructed dual-channel audio signal into the R and B layers of the digital image matrix, respectively. Furthermore, a new Chen memristor chaotic system solves the periodic window problems, such as the limited chaos range and nonuniform distribution. It can generate a mask block with high complexity and fill it into the G layer of the color image matrix to obtain a color audio image. In the next place, combining plaintext information with color audio images, interactive channel shuffling can not only weaken the correlation between adjacent samples, but also effectively resist selective plaintext attacks. Finally, the cryptographic block is used for overlapping diffusion encryption to fill the silence period of the speech signal, so as to obtain the ciphertext audio. Experimental results and comparative analysis show that the algorithm is suitable for different types of audio signals, and can resist many common cryptographic analysis attacks. Compared with that of similar audio encryption algorithms, the security index of the algorithm is better, and the efficiency of the algorithm is greatly improved.

Watermarking Scheme with CS Encryption for Security and Piracy of Digital Audio Signals

2017 ◽  
Vol 8 (4) ◽  
pp. 38-60 ◽  
Author(s):  
Rohit Thanki ◽  
Komal Borisagar
Keyword(s):  
Compressive Sensing ◽  
Audio Signal ◽  
Curvelet Transform ◽  
Digital Audio ◽  
Watermark Image ◽  
Audio Signals ◽  
Speech Watermarking ◽  
Dct Coefficients ◽  
Watermarking Scheme ◽  
Better Than

In this article, a watermarking scheme using Curvelet Transform with a combination of compressive sensing (CS) theory is proposed for the protection of a digital audio signal. The curvelet coefficients of the host audio signal are modified according to compressive sensing (CS) measurements of the watermarked data. The CS measurements of watermark data is generated using CS theory processes and sparse coefficients (wavelet coefficients of DCT coefficients). The proposed scheme can be employed for both audio and speech watermarking. The gray scale watermark image is inserted into the host digital audio signal when the proposed scheme is used for audio watermarking. The speech signal is inserted into the host digital audio signal when the proposed scheme is employed for speech watermarking. The experimental results show that proposed scheme performs better than the existing watermarking schemes in terms of perceptual transparency.

scholarly journals 402 Audio information retrieval for describing gait patterns in Brazilian horses

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 27-27
Author(s):  
Ricardo V Ventura ◽  
Rafael Z Lopes ◽  
Lucas T Andrietta ◽  
Fernando Bussiman ◽  
Julio Balieiro ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Subjective Evaluation ◽  
Audio Signal ◽  
Principal Component ◽  
Potential Method ◽  
Economic Sectors ◽  
Audio Features ◽  
Horse Industry ◽  
Audio Files ◽  
Audio Information

Abstract The Brazilian gaited horse industry is growing steadily, even after a recession period that affected different economic sectors in the whole country. Recent numbers suggested an increase on the exports, which reveals the relevance of this horse market segment. Horses are classified according to the gait criteria, which divide the horses in two groups associated with the animal movements: lateral (Marcha Picada) or diagonal (Marcha_Batida). These two gait groups usually show remarkable differences related to speed and number of steps per fixed unit of time, among other factors. Audio retrieval refers to the process of information extraction obtained from audio signals. This new data analysis area, in comparison to traditional methods to evaluate and classify gait types (as, for example, human subjective evaluation and video monitoring), provides a potential method to collect phenotypes in a reduced cost manner. Audio files (n = 80) were obtained after extracting audio features from freely available YouTube videos. Videos were manually labeled according to the two gait groups (Marcha Picada or Marcha Batida) and thirty animals were used after a quality control filter step. This study aimed to investigate different metrics associated with audio signal processing, in order to first cluster animals according to the gait type and subsequently include additional traits that could be useful to improve accuracy during the identification of genetically superior animals. Twenty-eight metrics, based on frequency or physical audio aspects, were carried out individually or in groups of relative importance to perform Principal Component Analysis (PCA), as well as to describe the two gait types. The PCA results indicated that over 87% of the animals were correctly clustered. Challenges regarding environmental interferences and noises must be further investigated. These first findings suggest that audio information retrieval could potentially be implemented in animal breeding programs, aiming to improve horse gait.

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 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 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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