Dr. Gerhard Schmidt

In recent years digital signal processing and speech processing in particular gained in importance for automotive applications. This is especially true for adaptive filters. They are utilized in applications such as acoustic echo cancellation, background noise reduction, beamforming for microphone arrays, or bandwidth extension for telephone bandlimited speech signals. In this talk, the importance of adaptive filters and their theory is highlighted by means of two application examples: hand-free telephones and systems for enhancing the communication quality within a passenger compartment.

A: Hands-free telephones started with just a loss control unit that allowed a half-duplex communication only. A full-duplex hands-free system requires an echo cancelling filter in parallel to the loudspeaker-enclosure-microphone system. The filter has to be matched to this highly time-variant system. A microphone array can improve the ratio of the local speech to the echo from the loudspeaker and the local noise. Local noise and echoes remaining due to filter mismatch are suppressed by a filter within the outgoing signal path. Even so these algorithms have been under research for several years it is still challenging to realize them in real products. Within the talk an overview on current algorithms and an outlook on forthcoming features for hands-free systems is given from an automotive perspective.

B: Due to a large amount of background noise the communication within a car driving at high or even moderate speed is often difficult. As a result of the high noise level the backseat passengers often lean towards the front passengers. Furthermore, all speakers increase their loudness. Even if both reactions enhance the quality of the “communication channel” it is rather exhausting and uncomfortable for the passengers. The situation can be improved by using in-car communication systems. These systems record the speech of each passenger by means of a single microphone or with an array of microphones. The recorded signals of the currently speaking passengers are processed by the system and played back via those loudspeakers which are located close to the non-active passengers. Comparable to public address systems, in-car communication systems operate within a closed electro-acoustic loop. Thus, signal processing is required to guarantee stable operation so as to avoid acoustic feedback such as howling or whistling. In the talk we describe the basic processing units of an in-car communication system. Those units contain mostly standard algorithms such as beamforming, echo cancellation, and loss control. However, these methods cannot be applied and controlled as in applications like hands-free telephones or preprocessing for speech recognition systems. Here, the problem is that the excitation signals and the distorting components are highly correlated – leading to convergence problems of adaptive algorithms. Furthermore, in-car communication systems have very restrictive demands on the tolerable processing delay.