Deep Scalogram Representations for Acoustic Scene Classification

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly,the features extracted from a subsequent fully connected layer are fed into(bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer;finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events(DCASE). On the evaluation set, an accuracy of 64.0 % from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0 % baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy,when fusing with a spectrogram-based system.

The role of watermelon caffeic acid O-methyltransferase (ClCOMT1) in melatonin biosynthesis and abiotic stress tolerance

Melatonin is a pleiotropic signaling molecule that regulates plant growth and responses to various abiotic stresses.The last step of melatonin synthesis in plants can be catalyzed by caffeic acid O-methyltransferase(COMT),a multifunctional enzyme reported to have N-acetylserotonin O-methyltransferase(ASMT)activity;however,the ASMT activity of COMT has not yet been characterized in nonmodel plants such as watermelon(Citrullus lanatus).Here,a total of 16 putative O-methyltransferase(ClOMT)genes were identified in watermelon.Among them,ClOMT03(Cla97C07G144540)was considered a potential COMT gene(renamed ClCOMT1)based on its high identities(60.00–74.93%)to known COMT genes involved in melatonin biosynthesis,expression in almost all tissues,and upregulation under abiotic stresses.The ClCOMT1 protein was localized in the cytoplasm.Overexpression of ClCOMT1 significantly increased melatonin contents,while ClCOMT1 knockout using the CRISPR/Cas-9 system decreased melatonin contents in watermelon calli.These results suggest that ClCOMT1 plays an essential role in melatonin biosynthesis in watermelon.In addition,ClCOMT1 expression in watermelon was upregulated by cold,drought,and salt stress,accompanied by increases in melatonin contents.Overexpression of ClCOMT1 enhanced transgenic Arabidopsis tolerance against such abiotic stresses,indicating that ClCOMT1 is a positive regulator of plant tolerance to abiotic stresses.

Self-attention transfer networks for speech emotion recognition

Background A crucial element of human-machine interaction,the automatic detection of emotional states from human speech has long been regarded as a challenging task for machine learning models.One vital challenge in speech emotion recognition(SER)is learning robust and discriminative representations from speech.Although machine learning methods have been widely applied in SER research,the inadequate amount of available annotated data has become a bottleneck impeding the extended application of such techniques(e.g.,deep neural networks).To address this issue,we present a deep learning method that combines knowledge transfer and self-attention for SER tasks.Herein,we apply the log-Mel spectrogram with deltas and delta-deltas as inputs.Moreover,given that emotions are time dependent,we apply temporal convolutional neural networks to model the variations in emotions.We further introduce an attention transfer mechanism,which is based on a self-attention algorithm to learn long-term dependencies.The self-attention transfer network(SATN)in our proposed approach takes advantage of attention transfer to learn attention from speech recognition,followed by transferring this knowledge into SER.An evaluation built on Interactive Emotional Dyadic Motion Capture(IEMOCAP)dataset demonstrates the effectiveness of the proposed model.