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Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

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Related Experiment Video

Updated: Oct 11, 2025

A Method to Study Adaptation to Left-Right Reversed Audition

A Method to Study Adaptation to Left-Right Reversed Audition

Published on: October 29, 2018

Improved prediction error expansion and mirroring embedded samples for enhancing reversible audio data hiding.

Yoga Samudra¹, Tohari Ahmad¹

¹Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

|November 29, 2021

Summary

This study enhances data security by embedding sensitive information into audio files. The improved method offers higher quality steganography and ensures data reversibility for secure cloud computing applications.

Keywords:

Audio processing Data hiding Data protection Information security Infrastructure

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Area of Science:

Computer Science
Information Security
Digital Forensics

Background:

Cloud computing and network applications process sensitive data, posing security and privacy risks due to inadequate public system mechanisms.
Data compromise leads to severe security and privacy issues for users, necessitating robust protection methods.
Existing data embedding techniques, like audio steganography, face limitations in payload capacity and cover data quality.

Purpose of the Study:

To address the limitations of existing steganography methods regarding payload capacity and stego-data quality.
To improve prediction error expansion-based algorithms and introduce a novel mirroring embedded sample scheme for audio steganography.
To achieve high-quality, reversible data embedding in audio covers for enhanced data security.

Main Methods:

Improved prediction error expansion algorithm.
Designed a mirroring embedded sample scheme to minimize distortion in the cover audio.
Focused on forcing processed audio samples to closely resemble original ones.

Main Results:

The proposed method significantly improves stego-data quality for larger payloads compared to existing algorithms.
Achieved a signal-to-noise ratio (SNR) exceeding 100 dB, surpassing that of compared methods.
Demonstrated the reversibility of the method, allowing full reconstruction of both payload and cover audio.

Conclusions:

The enhanced audio steganography method effectively balances payload capacity and cover data integrity.
The technique offers superior quality and reversibility, making it suitable for securing sensitive data in networked applications.
This research contributes a more secure and efficient approach to data hiding in audio signals.