Bayesian noise estimation in the modulation domain
MetadataShow full item record
Modulation domain has been reported to be a better alternative to time-frequency domain for speech enhancement, as speech intelligibility is closely linked with the modulation spectrum. Motivated by that, this paper investigates the use of modulation domain to model the noise density function. Results show that the modulation domain based Gamma density function better represents the noise density for all time-varying noise signals compared to the non-modulation domain. The modulation based Gamma density is then used to derive noise estimator via a Bayesian motivated MMSE approach. As the Gamma density closely matches the true noise spectrum in the modulation domain, the proposed noise estimator does not require bias compensation even for poor signal-to-noise ratio (SNR) conditions, i.e., = 5 dB. The proposed method yields better noise suppression compared to the state of the art methods and provides higher improvements.
Showing items related by title, author, creator and subject.
Singh, Maneesh Kumar (2017)Modulation domain has been reported to be a better alternative to Frequency domain for speech enhancement, as speech intelligibility is closely linked with the modulation spectrum. This suggests that the modulation spectrum ...
Event-Related Potentials of Single-Sided Deaf Cochlear Implant Users: Using a Semantic Oddball Paradigm in NoiseVoola, M.; Wedekind, A.; Nguyen, An ; Marinovic, Welber ; Rajan, G.; Tavora-Vieira, D. (2023)Introduction: In individuals with single-sided deafness (SSD), who are characterised by profound hearing loss in one ear and normal hearing in the contralateral ear, binaural input is no longer present. A cochlear implant ...
Brcic, Ramon (2002)This thesis addresses some problems that arise in signal processing when the noise is impulsive and follows a heavy tailed distribution. After reviewing several of the more well known heavy- tailed distributions the common ...