We approach the problem of implementing mixed-datatype support within the general matrix multiplication ( gemm ) operation of the BLAS-like Library Instantiation Software framework, whereby each matrix operand A , B , and C may be stored as single- or double-precision real or complex values. Another factor of complexity, whereby the matrix product and accumulation are allowed to take place in a precision different from the storage precisions of either A or B , is also discussed. We first break the problem into orthogonal dimensions, considering the mixing of domains separately from mixing precisions. Support for all combinations of matrix operands stored in either the real or complex domain is mapped out by enumerating the cases and describing an implementation approach for each. Supporting all combinations of storage and computation precisions is handled by typecasting the matrices at key stages of the computation—during packing and/or accumulation, as needed. Several optional optimizations are also documented. Performance results gathered on a 56-core Marvell ThunderX2 and a 52-core Intel Xeon Platinum demonstrate that high performance is mostly preserved, with modest slowdowns incurred from unavoidable typecast instructions. The mixed-datatype implementation confirms that combinatorial intractability is avoided, with the framework relying on only two assembly microkernels to implement 128 datatype combinations.

Different noise-reduction methods have been proposed in the literature for single and multiple-microphone applications. For binaural hearing aids, multiple-microphone noise-reduction methods offer two significant psycho-acoustical advantages with respect to the single-microphone methods. First, noise reduction strategies based on binaural processing (processing using the information received at the left and right ear) are more effective than using independent monaural processing because of the added information. Second, there is an user preference for noise reduction methods that preserve localization cues of both target and interfering signals. Although different multiple-microphone noise-reduction techniques have been proposed in the literature, only a small set is able to preserve the localization cues for both target and interfering signals. This paper proposes a binaural noise-reduction method that preserves the localization cues for both target and interfering signals. The proposed method is based on blind source separation (BSS) followed by a postprocessing technique inspired by a human auditory model. The performance of the proposed method is analyzed using objective and subjective measurements, and compared to existing binaural noise-reduction methods based on BSS and multichannel Wiener filter (MWF). Results show that for some scenarios and conditions, the proposed method outperforms on average the existing methods in terms of noise reduction and provides nearly similar sound quality.

For cell-phone applications, single microphone noise suppression techniques have limited performance at very low SNR (close to 0 dB). In certain cases, they also suffer from the artifacts of nonlinear processing. In this paper, we will show that techniques based on two-microphone blind source separation (BSS) algorithm provide significant interference suppression for cell-phone applications, particularly at low operating SNR values. We also propose a post-BSS processing method based on frequency-domain spectral subtraction that further improves the BSS speech output in diffused noise case. We optimize the BSS algorithm so that it can be implemented on a low-power audio codec processor, which would be ideal for cell phone applications. Furthermore, based on extensive analysis under different noise and acoustic conditions, we suggest recommendations for optimal placement of microphones on a cell phone. We also study the trade-off between the unmixing filter length and the noise suppression performance. In all our experiments, we use real recordings made on a cell phone equipped with two microphones.