Plastic multi-resolution auditory model based neural network for speech enhancement

Lai, Chen-Yen; Lo, Yu-Wen; Shen, Yih-Liang; Chi, Tai-Shih

doi:10.1109/apsipa.2017.8282096

Cited by 4 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms with a comparably high memory requirements are those based on trained models or data. Among those are localization algorithms [46,60], deep learning based speech enhancement and speech recognitionalgorithms [37,40,44,52]. As an example, the gaussian mixture model (GMM) of the localization algorithm requires about 90% of the total memory requirement of this algorithm [46,60].…”

Section: Work Total Detailsmentioning

confidence: 99%

A Survey on Application Specific Processor Architectures for Digital Hearing Aids

Gerlach

Payá-Vayá

Blume

2021

J Sign Process Syst

View full text Add to dashboard Cite

On the one hand, processors for hearing aids are highly specialized for audio processing, on the other hand they have to meet challenging hardware restrictions. This paper aims to provide an overview of the requirements, architectures, and implementations of these processors. Special attention is given to the increasingly common application-specific instruction-set processors (ASIPs). The main focus of this paper lies on hardware-related aspects such as the processor architecture, the interfaces, the application specific integrated circuit (ASIC) technology, and the operating conditions. The different hearing aid implementations are compared in terms of power consumption, silicon area, and computing performance for the algorithms used. Challenges for the design of future hearing aid processors are discussed based on current trends and developments.

show abstract