The increasing computing power provided by modern signal processors makes the application of digital signal processing algorithms more and more attractive in various fields, including hearing aids. This trend is evident in recent publications, e.g., [l-4], covering multichannel compression and loudness compensation techniques among others. Power consumption is a parameter of major concern in hearing aid equipment. This of course is not in favor of digital signal processing approaches due to the additional need for signal conversion from analog to digital and vice versa. Hearing aid equipment requires an audio signal with a bandwidth of about 6 kHz and a dynamic range of about 80 dB. The power consumption for an adequately specified analog-to-digital (A/D) converter including a suitable anti-aliasing filter should be considerably less than 1 mW. A/D converters with such a low power consumption and a 14-bit resolution at above 10 kHz sampling rate are clearly not standard components. Instrumentation-type A/D converters are understood to provide a resolution according to the number of specified bits; i.e., the upper bound for quantization noise is expected to be half the least significant bit. Furthermore, anti-aliasing filters in such applications are required to keep aliasing below that same bound. Fortunately, in audio applications the requirements may be loosened without loss of perceived signal quality. One aspect is resolution. Numerous masking experiments  indicate that quantization noise at a level of 25 dB below signal components does not cause any perceptual degradation. Consequently, an adaptive 6-bit quantizer  will do as shown in the front end section of Fig. 1, i.e., left of the codeword memory labeled M, to M5. Another aspect is aliasing. An attractive design approach includes oversampling and realizing antialiasing by a digital filter. In the case of audio signals a sampling rate of 32 kHz and subsequent low-pass filtering and downsampling to 16 kHz will do. The resulting fold-over frequency at 8 kHz allows rather loose constraints on the decimation filter. There are two reasons for this. Most sounds reveal a spectrum that falls off at a considerable rate above 8 kHz. Exceptions to this mainly comprise fricative sounds. However, moderate aliasing is perceptually not relevant in this case. Consequently, a simple nonrecursive digital filter with favorable implementation properties is successfully used in the proposed design. It shows 6 dB attenuation at 8 kHz fold-over frequency, 30 dB at 11 kHz, and side lobes below 40 dB. The perceptual requirements are very well met in this way. This digital lowpass filter is indicated in Fig. 1 on the right side of the codeword memory. The remaining parts of this paper are organized as follows: details of front end processing are described in Section 2. Section 3 describes how the codeword memory is organized. In particular, this section shows how 14-bit-wide codewords are generated by combining the information supplied by quantizer and gain control. The decimation filter is described in detail in Section 4 and conclusions are summarized in Section 5.