Abstract
Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of hearing aid users by restoring speech intelligibility. An open problem in today’s commercial hearing aids is how to take into account users’ preferences, indicating which acoustic sources should be suppressed or enhanced, since they are not only user-specific but also depend on many situational factors. In this paper, we develop a fully probabilistic approach to “situated soundscaping”, which aims at enabling users to make on-the-spot (“situated”) decisions about the enhancement or suppression of individual acoustic sources. The approach rests on a compact generative probabilistic model for acoustic signals. In this framework, all signal processing tasks (source modeling, source separation and soundscaping) are framed as automatable probabilistic inference tasks. These tasks can be efficiently executed using message passing-based inference on factor graphs. Since all signal processing tasks are automatable, the approach supports fast future model design cycles in an effort to reach commercializable performance levels. The presented results show promising performance in terms of SNR, PESQ and STOI improvements in a situated setting.
Highlights
It should be noted that the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility measure (STOI) metrics are by definition average group metrics
In this paper we presented a probabilistic modeling framework for situated design of personalized soundscaping algorithms for hearing aids
Since hearing aids are resource constrained devices, we proposed a very compact generative model for acoustic mixtures and execute approximate inference in real-time through efficient message passing-based methods
Summary
The ideal noise reduction or speech enhancement algorithm depends on the lifestyle and living environment of the hearing aid user. Personalization of these algorithms is very difficult to achieve in advance. When a hearing aid’s noise reduction algorithm fails to perform well under these conditions, it would be desirable to let the user record on the spot a short segment of the background chatter and instantly design an algorithm that uses the characteristics of the recorded signal to better suppress similar background noise signals during the ongoing conversation. We call this on-the-spot user-driven algorithm design process “situated soundscaping”, where a user can generate her own noise reduction algorithm on the spot and shape her perceived acoustic environment (“soundscaping”) by adjusting source-specific gains according to her preferences
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