Speech scrambling based on multiwavelet and Arnold transformations

Abstract

For communication applications where secure speech signal transmissions are a key requirement, speech scrambler is taken into consideration. To prevent someone from listening in on private conversations without their knowledge, it can transform clear speech into a signal that is unintelligible. The proposed speech scrambling system involves using two types of frequency transformation techniques: multiwavelet transform and Arnold transform. The effectiveness of the scrambling algorithm was evaluated with the help of three different measurements: the peak signal to noise ratio (PSNR), the estimated time (ET), and the mean square error (MSE). According to the final findings, the outcome of the scrambled speech signal does not have any residual intelligibility, while the quality of the descrambled speech is extremely satisfactory and has a low MSE level.

Enhancing Speech Privacy through Dynamic Chaos- Driven Scrambling with Nested Segmentation

“…While effective for signals of limited length, generating long chaotic signals may result in sequences with duplicated values, thereby constraining scrambling possibilities. Furthermore, the Arnold Transform (AT) has been utilized for signal permutation in various studies [16][17][18][19]. While AT offers versatility, it is constrained by inflexibility and limitations in handling irregular data, necessitating additional effort for application.…”

“…Consequently, tent map awards a key space up to 5.8x10 14 x 10 16 . As result, the full key space of our proposed speech cryptosystem is a combination of the obtained key spaces for each factor and equals 4.1x10 51 ≈2 174 (approximately 𝑙𝑜𝑔 2 (10 51 ) ≈ 170).…”