Evaluation of digital watermarking on subjective speech quality.
Sci Rep
; 11(1): 20185, 2021 10 12.
Article
en En
| MEDLINE
| ID: mdl-34642471
New methods of securing the distribution of audio content have been widely deployed in the last twenty years. Their impact on perceptive quality has, however, only been seldomly the subject of recent extensive research. We review digital speech watermarking state of the art and provide subjective testing of watermarked speech samples. Latest speech watermarking techniques are listed, with their specifics and potential for further development. Their current and possible applications are evaluated. Open-source software designed to embed watermarking patterns in audio files is used to produce a set of samples that satisfies the requirements of modern speech-quality subjective assessments. The patchwork algorithm that is coded in the application is mainly considered in this analysis. Different watermark robustness levels are used, which allow determining the threshold of detection to human listeners. The subjective listening tests are conducted following ITU-T P.800 Recommendation, which precisely defines the conditions and requirements for subjective testing. Further analysis tries to determine the effects of noise and various disturbances on watermarked speech's perceived quality. A threshold of intelligibility is estimated to allow further openings on speech compression techniques with watermarking. The impact of language or social background is evaluated through an additional experiment involving two groups of listeners. Results show significant robustness of the watermarking implementation, retaining both a reasonable net subjective audio quality and security attributes, despite mild levels of distortion and noise. Extended experiments with Chinese listeners open the door to formulate a hypothesis on perception variations with geographical and social backgrounds.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Reconocimiento de Normas Patrones Automatizadas
/
Compresión de Datos
Límite:
Humans
País/Región como asunto:
Asia
Idioma:
En
Revista:
Sci Rep
Año:
2021
Tipo del documento:
Article
País de afiliación:
República Checa
Pais de publicación:
Reino Unido