Speech enhancement via mask-mapping based residual dense network

Abstract Masking-based and spectrum mapping-based methods are the two main algorithms of speech enhancement with deep neural network (DNN). But the mapping-based methods only utilizes the phase of noisy speech, which limits the upper bound of speech enhancement performance. Masking-based methods need to accurately estimate the masking which is still the key problem. Combining the advantages of above two types of methods, this paper proposes the speech enhancement algorithm MM-RDN (masking-mapping residual dense network) based on masking-mapping (MM) and residual dense network (RDN). Using the logarithmic power spectrogram (LPS) of consecutive frames, MM estimates the ideal ratio masking (IRM) matrix of consecutive frames. RDN can make full use of feature maps of all layers. Meanwhile, using the global residual learning to combine the shallow features and deep features, RDN obtains the global dense features from the LPS, thereby improves estimated accuracy of the IRM matrix. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, MM-RDN can still outperform the existing convolutional recurrent network (CRN) method in the measures of perceptual evaluation of speech quality (PESQ) and other evaluation indexes. It indicates that the proposed algorithm is more generalized in untrained conditions

Construction of Efficient Deep Blue Aggregation-Induced Emission Luminogen from Triphenylethene for Nondoped Organic Light-Emitting Diodes

Deep blue emitters are crucial for full color displays and organic white lighting. Thanks to the research efforts by scientists, many efficient light emitters with aggregation-induced emission (AIE) characteristics have been synthesized and found promising applications in organic light-emitting diodes (OLEDs). However, few AIE emitters with deep blue emissions and excellent electroluminescence (EL) performance have been reported. The contribution here reports a simple but successful molecular design strategy for synthesizing efficient solid-state emitters for nondoped OLEDs with both deep blue and white emissions. This strategy utilizes triphenylethene, a weakly conjugated AIE luminogen, as building block for constructing deep blue emitter, involving no complicated control of emission color through adjustment of the steric hindrance of chromophores, and enables a wide selection of partnered functional units. The synthesized AIE luminogen, abbreviated as BTPE-PI, is thermally stable and exhibits high fluorescence quantum efficiency as well as good charge injection capability in the solid state. Nondoped deep blue OLED fabricated from BTPE-PI shows a very high external quantum efficiency of 4.4% with a small roll-off, whose performance is the best among deep blue AIE materials reported so far. An efficient white OLED with Commission Internationale de l’Eclairage (CIE) coordinates of (0.33, 0.33) at theoretical white point was first achieved by using AIE luminogen BTPE-PI as deep blue emitter. Such molecular design strategy opens a new avenue in the development of efficient solid-state deep blue emitters for nondoped OLED applications