SASEGAN-TCN: Speech enhancement algorithm based on self-attention generative adversarial network and temporal convolutional network.

Traditional unsupervised speech enhancement models often have problems such as non-aggregation of input feature information, which will introduce additional noise during training, thereby reducing the quality of the speech signal. In order to solve the above problems, this paper analyzed the impact of problems such as non-aggregation of input speech feature information on its performance. Moreover, this article introduced a temporal convolutional neural network and proposed a SASEGAN-TCN speech enhancement model, which captured local features information and aggregated global feature information to improve model effect and training stability. The simulation experiment results showed that the model can achieve 2.1636 and 92.78% in perceptual evaluation of speech quality (PESQ) score and short-time objective intelligibility (STOI) on the Valentini dataset, and can accordingly reach 1.8077 and 83.54% on the THCHS30 dataset. In addition, this article used the enhanced speech data for the acoustic model to verify the recognition accuracy. The speech recognition error rate was reduced by 17.4%, which was a significant improvement compared to the baseline model experimental results.

Removal of cationic dyes from aqueous solution using polyacrylic acid modified hemp stem.

Water pollution caused by dyes is a pressing environmental challenge due to their persistence and difficulty in degradation. Herein, an anionic adsorbent (HS-PAANa) was synthesized by grafting polyacrylic acid (PAA) onto the agricultural waste-hemp stem (HS). The obtained HS-PAANa adsorbent exhibited rapid adsorption kinetics, high adsorption capacity, and a favorable preference for cationic dyes, such as methylene blue (MB) and crystal violet (CV). The experimental data fit well with the pseudo-second-order kinetic model and Langmuir isotherm, demonstrating the efficiency of HS-PAANa in dye removal. Notably, the optimal adsorption capacities of HS-PAANa for MB and CV were found to be 1296.65 mg/g and 1451.43 mg/g, respectively. In the cationic/anionic dyes (MB/MO) binary systems, HS-PAANa exhibited enhanced selective adsorption of cationic dyes (MB), indicating its potential for targeted removal of specific dyes from mixed solutions. Moreover, HS-PAANa adsorption shows an excellent recyclability, after five cycles, HS-PAANa still maintained MB and CV removal rates of 93.85% and 95.08%, respectively. Therefore, the bioadsorbent HS-PAANa exhibits high potential as a highly efficient adsorbent for the effective treatment of cationic pollutants in wastewater.