Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article accepted for publication in IET Signal Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusion

The Characterised Noise Hi source finder: Detecting Hi galaxies using a novel implementation of matched filtering

The spectral line datacubes obtained from the Square Kilometre Array (SKA) and its precursors, such as the Australian SKA Pathfinder (ASKAP), will be sufficiently large to necessitate automated detection and parametrisation of sources. Matched filtering is widely acknowledged as the best possible method for the automated detection of sources. This paper presents the Characterised Noise Hi (CNHI) source finder, which employs a novel implementation of matched filtering. This implementation is optimised for the 3-D nature of the planned Wide-field ASKAP Legacy L-band All- sky Blind surveY's (WALLABY) Hi spectral line observations. The CNHI source finder also employs a novel sparse representation of 3-D objects, with a high compression rate, to implement Lutz one-pass algorithm on datacubes that are too large to process in a single pass. WALLABY will use ASKAP's phenomenal 30 square degree field of view to image \sim 70% of the sky. It is expected that WALLABY will find 500 000 Hi galaxies out to z \sim 0.2.Comment: Part of the 2012 PASA Source Finding Special Issue, 10 figure