Tim Haulick

Signal processing for in-car communication systems

Abstract Due to a large amount of background noise the communication within a car driving at high or even moderate speed is often difficult. This is especially true if one of the communication partners is the driver and the other is one of the backseat passengers. 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 this contribution 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.

Noise suppression for automotive applications based on directional information

In noise suppression systems for automotive applications, the use of adaptive beamformers has proven to be of great potential. Nevertheless, in diffuse noise fields the amount of noise attenuation is rather limited and depends on the number of microphones. In order to enhance the signal-to-noise ratio further, additional classical noise suppression schemes, like spectral subtraction, are often applied. Unfortunately, these schemes tend either to introduce speech distortions or to leave a large amount of residual noise. We describe a method of extracting additional spatial information from a conventional beamformer in a generalized sidelobe structure. This spatial information can be utilized, e.g., to control parameters, like overestimation or spectral floor, of classical noise suppression schemes in a frequency selective manner or to compute a simple attenuation factor for suppressing nonstationary noise. An outlook is given on further usage of the spatial information in other algorithmic parts of a hands-free telephone or a speech recognition system.

Self-calibrating microphone arrays for speech signal acquisition: a systematic approach

The application of microphone arrays and beamforming techniques for speech acquisition promises significant improvement compared to systems operating with a single microphone. Adaptive beamformers offer a potentially superior performance to fixed beamformers particularly in the case of time varying sound field characteristics or in the case of coherent noise such as interfering speakers, loudspeaker signals, etc. However, for real-world applications adaptive beamformers hold the risk of severe signal degradation. Disturbances such as mismatched microphones, an imprecise steering direction or reverberation due to multi-path propagation may cause an adaptive beamformer to distort the desired signal. Microphone mismatch naturally arises from production tolerances as well as from aging effects in the long run.This contribution presents a class of adaptive self-calibration methods. These methods perform a calibration in the background during normal operation of the system and therefore save the need for an additional costly calibration procedure. Based on a systematic approach, new configurations as well as some well-known configurations are derived. The performance of the different self-calibration configurations is examined in a car environment.

A compact microphone array system with spatial post-filtering for automotive applications

Compact microphone arrays allow for directional filtering with a minimum of installation space. They are therefore particularly suitable for automotive applications. Typically, compact arrays are realized as differential arrays or filter-and-sum beamformers which both show limited performance in terms of directivity. In this contribution we present a novel system for directional filtering for compact arrays. This system consists of two closely spaced microphones and incorporates an adaptive beamformer as well as a spatial post-filter which is designed to suppress non-stationary noise.