Audio Cryptography via Enhanced Genetic Algorithm

As communication technologies surged recently, the secrecy of shared information between communication parts has gained tremendous attention. Many Cryptographic techniques have been proposed/implemented to secure multimedia data and to allay public fears during communication. This paper expands the scope of audio data security via an enhanced genetic algorithm. Here, each individual (audio sample) is genetically engineered to produce new individuals. The enciphering process of the proposed technology acquires, conditions, and transforms each audio sample into bit strings. Bits fission, switching, mutation, fusion, and deconditioning operations are then applied to yield cipher audio signals. The original audio sample is recovered at the receiver's end through a deciphering process without the loss of any inherent message. The novelty of the proposed technique resides in the integration of fission and fusion into the traditional genetic algorithm operators and the use of a single (rather than two) individual(s) for reproduction. The effectiveness of the proposed cryptosystem is demonstrated through simulations and performance analyses.

Shared Sample Projects (Advanced)

The lesson described in this chapter is meant to give students the opportunity to think about and develop their sonic imaginations, critical listening, decision making (including the skill of fighting option paralysis), ideas about living in a recording-saturated world, and authority and ownership of recorded music. Designed for undergraduate students, this activity requires all students to use the same short audio sample and audio manipulation software to create new full-length pieces of original music using the shared sample and nothing else. They can process and manipulate the sample as they see fit, but they may not use any additional sounds or instruments.

HOW TO SPOT COVID-19 PATIENTS: SPEECH & SOUND AUDIO ANALYSIS FOR PRELIMINARY DIAGNOSIS OF SARS-COV-2 CORONA PATIENTS

Background: The global cases of Covid-19 increasing day by day. On Nov. 25, 2020, a total of 59,850,910 cases reported globally with a 1,411,216 global death. In India, total cases in the country now stand at 91,77,841 including 86,04,955 recoveries and 4,38,667 active cases as of Nov. 24, 2020, as per data issued by ICMR. A new generation of voice/audio analysis application which can tell whether the person is suffering from COVID-19 or not. Aims: To describe how to establish a new generation of voice/audio analysis applications to identify the suspected covid-19 hidden cases in hotspot areas with the help of an audio sample of the general public. Materials & Methods: The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public. Results: The collection of the audio sample will be done from the already suffered covid-19 patients in (.Wave files) personally or through phone calls. The audio samples like the sound of the cough, the pattern of breathing, respiration rate, and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody, and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample. Discussion: The voice/audio application current project can be merged with a mobile App called “Aarogya Setu” by Govt. of India. The project can be implemented in the high-risk area of Covid-19 in the country. Conclusion: This new method of detecting cases will decrease the workload in the covid-19 laboratory.

Multi-Modality Emotion Recognition Model with GAT-Based Multi-Head Inter-Modality Attention

Emotion recognition has been gaining attention in recent years due to its applications on artificial agents. To achieve a good performance with this task, much research has been conducted on the multi-modality emotion recognition model for leveraging the different strengths of each modality. However, a research question remains: what exactly is the most appropriate way to fuse the information from different modalities? In this paper, we proposed audio sample augmentation and an emotion-oriented encoder-decoder to improve the performance of emotion recognition and discussed an inter-modality, decision-level fusion method based on a graph attention network (GAT). Compared to the baseline, our model improved the weighted average F1-scores from 64.18 to 68.31% and the weighted average accuracy from 65.25 to 69.88%.

Anti-Forensics of Double Compressed MP3 Audio

The widespread availability of audio editing software has made it easy to create acoustically convincing digital audio forgeries. To address this problem, more and more attention has been paid to the field of digital audio forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. The compression history of an audio sample can be used to provide evidence of audio forgeries. In this work, we present a simple method for distinguishing the MP3 compression history of an audio sample. We show the proposed anti-forensics method to remove the artifacts of MP3 double compression by destroying the audio frame structure. In addition, effectiveness of the proposed method is verified by three double compression detection methods. The experimental results show that the proposed method can effectively resist detection from three methods.

Landmark-based audio fingerprinting system applied to vehicle squeak and rattle noises

This article investigates whether the robust landmark-based audio fingerprinting method created for recognizing music can be applied to identify squeak and rattle (S&R) types of sounds. The identification is performed by matching a query audio sample to the perceptually closest audio sample that is stored in a pre-developed database of S&R audio sounds. The aim of the application of the method in the automotive industry is to facilitate the process S&R experts go through during sound identification. The experimental results show that the algorithm can be used for identification of different types of S&R sounds when the audio database contains a limited number of reference samples.

Various Approaches in Musical Instrument Identification

In the music information retrieval (MIR) field, it is highly desirable to know what instruments are used in an audio sample. Musical instrument classification is one of the sub domains of music information retrieval. Many researchers have presented different approaches for identifying western instruments and those approaches proved to be good for instrument identification. In this article, we have presented work done by the various authors to identify musical instrument using various approaches such sparse based representation, bio-inspired hierarchical model, joint modelling, Bayesian networks, neural networks, convolution neural networks, individual partials, clustering, and segmentation.

Information Hiding Based on Audio Steganography using Least Significant Bit

Steganography is the idea of hiding private or sensitive data or information within something that appears to be nothing out of the normal. A few problems arise especially in securing data and information when the information had been lost or stolen from unauthorized user. Traditionally, we give information manually using paper; it is possible that the information could be stolen by unauthorized user. The main objective of this study is to hide secret information in audio, so that other persons will not notice the presence of the information. The proposed method of this study is by using Least Significant Bit (LSB) algorithm to design an audio steganography. In the proposed method, each audio sample is converted into bits and then the text data is embedded. The expected result of this study will produce a steganography audio that will be able to hide data or information efficiently from unauthorized user, also to ensure the safety of the information in an authorized hand.