Sound Field Classification in Small Microphone Arrays
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Sound Field Classification in Small Microphone Arrays
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AIA-DAGA 2013 Merano
Sound Field Classification in Small Microphone Arrays Roman Scharrer and Janina Fels Institute of Technical Acoustics, RWTH Aachen University, Aachen, Germany
Introduction A wide range of signal processing strategies are used in modern hearing aids and other mobile devices such as mobile phones to increase speech intelligibility, reduce noise and thus increase user satisfaction. Most of these strategies are beneficial in some but not all situations. If these strategies are not adjusted correctly, they might even disturb the user. Therefore, the performance of these signal processing strategies depends to a large extent on several boundary conditions like the signal itself or the sound field [1]. Typical properties that influence the performance of common signal processing strategies in mobile devices are the signal-to-noise-energy-ratio (SNR), the reverberation time, and the direct-to-reveberant-energy-ratio (DRR) [4]. The active-to-reactive-energy ratio (ARR) also influences some signal processing strategies. All these properties are a by-product of the sound field classification and can be received at no additional cost. The main advantage of the sound field classification, however, lies in the automatic program selection, whose performance can be increased significantly by using additional information on the sound field above signal properties alone [11].
approach focuses more on classifying the background noise rather than the acoustic environment.
Sound Field Classification The proposed sound field classification is based on the sound field indicators as introduced by [5]. The calculation of those indicators in small microphone arrays is explained in [9]. The basic assumption is that the sound field is a combination of four components. The total sound field energy Etotal is then distributed on the four components Etotal = Efree + Ediffuse + Ereactive + Enoise
(1)
The feature vector SFD consists of three features as follows: |γpp | (2) SFD = |
