The paper describes a new multichannel sound interface for the BeagleBone Black, Cape4all. The sound interface has 6 input channels with optional microphone pre-amplifiers and between 4 and 6 output channels. The multichannel sound extension cape for the BeagleBone Black is designed and produced. An ALSA driver is written for it. It is used with the openMHA hearing aid research software to perform hearing aid signal processing on the BeagleBone Black with a customized Debian distribution tailored to real-time audio signal processing.

Localization algorithms have become of considerable interest for robot audition, acoustic navigation, teleconferencing, speaker localization, and many other applications over the last decade. In this paper, we present a real-time implementation of a Gaussian mixture model (GMM) based probabilistic sound source localization algorithm for a low-power VLIW-SIMD processor for hearing devices. The algorithm has been proven to allow for robust localization of multiple sound sources simultaneously in reverberant and noisy environments. Real-time computation for audio frames of 512 samples at 16 kHz was achieved by introducing algorithmic optimizations and hardware customizations. To the best of our knowledge, this is the first real-time capable implementation of a computationally complex GMM-based sound source localization algorithm on a low-power processor. The resulting estimated core area without consideration of memory in 40nm low-power TSMC technology is 188,511 pm 2 .

One of the key problems for hearing impaired persons represents the cocktail party scenario, in which a bilateral conversation is surrounded by other speakers and noise sources. State-of-the-art beamforming techniques are able to segregate specific sound sources from the environment, presupposing the position of the speaker. The speaker position can be estimated in the frontal azimuth-plane with a probabilistic localization algorithm from the binaural microphone input of the both-eared hearing aid system. However, the binaural speaker localization requires computationally complex audio processing and filtering. The high computational complexity combined with low energy requirements to meet the battery constraints of hearing aid devices presents an implementation challenge.This paper proposes a customized C programmable processor design to implement the speaker localization algorithm that fulfills the challenging requirements placed by the usage context. When compared to a VLIW-based processor design with similar basic computational resources and no special instructions, the proposed processor reaches a 151x speed-up. For a 28nm standard CMOS technology, power consumption of 12 mW (at 50 MHz) and silicon area of 0.3 mm(2) is estimated. This is the first publication of a realistic programmable processing architecture for the probabilistic binaural speaker localization or a comparably complex algorithm for hearing aid devices. The algorithms supported by the previously proposed implementations are approximately 15x less computationally demanding.

Current research on audio signal processing algorithms for digital hearing aid devices is extremely pushing the performance demands. Nowadays, there is a trend of using several microphones in such systems (e.g., binaural systems) to improve the speech perception of a hearing impaired person. However, there is a lack of mobile platforms, capable of processing such algorithms in real-time. This paper presents a new mobile SoC-based evaluation and development platform (including a multi-channel audio extension board), specially thought not only for evaluating new hearing aid signal processing algorithms but also to develop new hardware co-processor architectures, that could be integrated in current hearing aid devices to improve their performance with a minimal extra energy consumption.