Recently, the time-domain-based methods (i.e., the method of modeling the raw waveform directly) for audio source separation have shown tremendous potential. In this paper, we propose a model which combines the complexed spectrogram domain feature and time-domain feature by a cross-domain encoder (CDE) and adopts the hierarchic temporal convolutional network (HTCN) for multiple music sources separation. The CDE is designed to enable the network to code the interactive information of the time-domain and complexed spectrogram domain features. HTCN enables it to learn the long-time series dependence effectively. We also designed a feature calibration unit (FCU) to be applied in the HTCN and adopted the multi-stage training strategy during the training stage. The ablation study demonstrates the effectiveness of each designed component in the model. We conducted the experiments on the MUSDB18 dataset. The experimental results indicate that our proposed CDE-HTCN model outperforms the top-of-the-line methods and, compared with the state-of-the-art method, DEMUCS, achieves the improvement of the average SDR score of 0.61 dB. Significantly, the improvement of the SDR score for the $\ bass$ source has a sizable margin of 0.91 dB.

The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models.

Recently, many Transformer-style dual-path models have achieved impressive performance for speech enhancement. However, their high parameters and computational complexity hinder their practical application. In this letter, we propose a monaural speech enhancement network with lower parameter count and complexity based on high-order information interaction and feature calibration (IIFC-Net). The network includes high-order information interaction Transformer (HOIIFormer) with high-order information interaction (HOII) block instead of a multi-head self-attention (MHSA) in Transformer. IIFC-Net leverages dual-path HOIIFormer (DPH) to model the distant dependency relation along time and frequency dimensions, respectively, and effectively captures deep-level information through the HOII block. We also design a feature calibration (FC) block to enhance the frequency components of target speech, which can be verified by a visualization analysis. The outcomes of experiments conducted on the VoiceBank+DEMAND and WHAMR! datasets demonstrate that IIFC-Net achieves comparable performance in terms of denoising, dereverberation, and simultaneous denoising & dereverberation

This report presented a patient with 2 long-term broken roots displaced in left maxillary sinus. The residual root fragments made the patient uncomfortable in both mind and body and interfered with prosthodontics work. The application of endoscope combined with piezoelectric device both helps in removing the broken roots successfully with minimally surgical injury and preserves the residual alveolar bone. Computed tomography scans and 3-dimensional reconstructions located the broken roots. A 1.0 cm × 1.5 cm rectangle bone window on anterolateral sinus wall was opened by a piezoelectric device to place the endoscope and forcep into sinus. Two broken roots could be observed clearly via a endoscopic screen. They were removed by a mini goblet forcep completely and efficiently. A whole bone lid was replaced with a biological membrane to help repair bone defect after removing procedure. The operation is about 20 minutes with endoscope and piezoelectric device helped to save a lot of time and provided excellent visual surgical field. Main postoperative adverse effects were swelling, numbness, and temporal no-vitality for the first premolar (24). Three months later, computed tomography shows the Schneiderian membrane thinned to around 0.8 mm. The bone lid is on its position and starts to perform synostosis. The 24 tooth is still dysesthetic and needs time to recover. Endoscopic surgery combined with a piezoelectric device has obvious advantage of minimizing surgical injury and providing excellent visibility of surgical field when removing long-term foreign bodies in maxillary sinus. It is efficient and protects the residual alveolar bone.