Sound Field Classification in Small Microphone Arrays Using Spatial Coherences

The quality and performance of many multi-channel signal processing strategies in microphone arrays as well as mobile devices for the enhancement of speech intelligibility and audio quality depends to a large extent on the acoustic sound field that they are exposed to. As long as the assumption on the sound field is not met, the performance decreases significantly and may even yield worse results for the user than an unprocessed signal. Current hearing aids provide the user for instance with different programs to adapt the signal processing to the acoustic situation. Signal classification describes the signal content and not the type of sound field. Therefore, a further classification of the sound field, in addition to the signal classification, would increase the possibilities for an optimal adaption of the automatic program selection and the signal processing methods in mobile devices. To this end a sound field classification method is proposed that is based on the complex coherences between the input signals of distributed acoustic sensors. In addition to the general approach an exemplary setup of a hearing aid equipped with two microphone sensors is discussed. As only coherences are used, the method classifies the sound field regardless of the signal carried by it. This approach complements and extends the current signal classification approach used in common mobile devices. The method was successfully verified with simulated audio input signals and with real life examples.