SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint

Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry. In automatic song writing, lyric-to-melody generation and melody-to-lyric generation are two important tasks, both of which usually suffer from the following challenges: 1) the paired lyric and melody data are limited, which affects the generation quality of the two tasks, considering a lot of paired training data are needed due to the weak correlation between lyric and melody; 2) Strict alignments are required between lyric and melody, which relies on specific alignment modeling. In this paper, we propose SongMASS to address the above challenges, which leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling for lyric-to-melody and melody-to-lyric generation. Specifically, 1) we extend the original sentence-level MASS pre-training to song level to better capture long contextual information in music, and use a separate encoder and decoder for each modality (lyric or melody); 2) we leverage sentence-level attention mask and token-level attention constraint during training to enhance the alignment between lyric and melody. During inference, we use a dynamic programming strategy to obtain the alignment between each word/syllable in lyric and note in melody. We pre-train SongMASS on unpaired lyric and melody datasets, and both objective and subjective evaluations demonstrate that SongMASS generates lyric and melody with significantly better quality than the baseline method without pre-training or alignment constraint

A transformer acoustic signal analysis method based on matrix pencil and hybrid deep neural network

Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%

Detecting somatisation disorder via speech: introducing the Shenzhen Somatisation Speech Corpus

Objective Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisation disorder recognition. The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies. To this end, we introduce our somatisation disorder speech database and give benchmark results. Methods By collecting speech samples of somatisation disorder patients, in cooperation with the Shenzhen University General Hospital, we introduce our somatisation disorder speech database, the Shenzhen Somatisation Speech Corpus (SSSC). Moreover, a benchmark for SSSC using classic acoustic features and a machine learning model is proposed in our work. Results To obtain a more scientific benchmark, we have compared and analysed the performance of different acoustic features, i. e., the full ComParE feature set, or only MFCCs, fundamental frequency (F0), and frequency and bandwidth of the formants (F1-F3). By comparison. the best result of our benchmark is the 76.0 % unweighted average recall achieved by a support vector machine with formants F1–F3. Conclusion The proposal of SSSC bridges a research gap in somatisation disorder, providing researchers with a publicly accessible speech database. In addition, the results of the benchmark show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders

Effect of direct surgical treatment in pregnancy-related uterine arteriovenous malformation

Abstract Background Uterine arteriovenous malformation (UAVM) is a relatively rare but potentially life-threatening situations abnormal vascular connections between the uterine arterial and venous systems. Lack of recognized guidelines and clinic experience, there is a lot of clinic problems about diagnosis and treatment. By analyzing the clinical data of patients with pregnancy-related UAVM, we aim to confirm the safety of direct surgeries and the benefit of pretreatment (uterine artery embolization or medical therapy) before surgery, and to explore more optimal therapies for patients with pregnancy-related UAVM. Methods A total of 106 patients in Qilu Hospital of Shandong University from January 2011 to December 2021 diagnosed of pregnancy-related UAVM were involved in this study. Depending on whether preoperative intervention was performed, the patients were divided into direct surgery group and pretreatment group (uterine artery embolization or medical management). Clinical characteristics, operative related factors and prognosis were analyzed. Results The most common symptom of pregnancy-related UAVM was vaginal bleeding (82.5%), which could also be accompanied by abdominal pain. Pretreatments (uterine artery embolization or medical therapy) had no obvious benefit to the subsequent surgeries, but increased the hospital stay and hospital cost. Direct surgery group had satisfactory success rate and prognosis compared to pretreatment group. Conclusion For pregnancy-related UAVM, direct surgery has good effects and high safety with shorter hospital stays and less hospital cost. What is more, without uterine artery embolization and other medical therapy, patients could remain better fertility in future

ORYZA SATIVA SPOTTED-LEAF 41 (OsSPL41) Negatively Regulates Plant Immunity in Rice

Identification of immunity-associated leucine-rich repeat receptor-like protein kinases (LRR-RLK) is critical to elucidate the LRR-RLK mediated mechanism of plant immunity. Here, we reported the map-based cloning of a novel rice SPOTTED-LEAF 41 (OsSPL41) encoding a putative LRR-RLK protein (OsLRR-RLK41/OsSPL41) that regulated disease responses to the bacterial blight pathogen Xanthomonas oryzae pv. oryzae (Xoo). An 8-bp insertion at position 865 bp in a mutant spotted-leaf 41 (spl41) allele led to the formation of purple-brown lesions on leaves. Functional complementation by the wild type allele (OsSPL41) can rescue the mutant phenotype, and the complementary lines showed similar performance to wild type in a number of agronomic, physiological and molecular indices. OsSPL41 was constitutively expressed in all tissues tested, and OsSPL41 contains a typical transmembrane domain critical for its localization to the cell membrane. The mutant exhibited an enhanced level of resistance to Xoo in companion of markedly up-regulated expression of pathogenesis-related genes such as OsPR10a, OsPAL1 and OsNPR1, while the level of salicylic acid was significantly increased in spl41. In contrast, the over-expression lines exhibited a reduced level of H2O2 and were much susceptible to Xoo with down-regulated expression of pathogenesis-related genes. These results suggested that OsSPL41 might negatively regulate plant immunity through the salicylic acid signaling pathway in rice