A Compact and Discriminative Feature Based on Auditory Summary Statistics for Acoustic Scene Classification

One of the biggest challenges of acoustic scene classification (ASC) is to find proper features to better represent and characterize environmental sounds. Environmental sounds generally involve more sound sources while exhibiting less structure in temporal spectral representations. However, the background of an acoustic scene exhibits temporal homogeneity in acoustic properties, suggesting it could be characterized by distribution statistics rather than temporal details. In this work, we investigated using auditory summary statistics as the feature for ASC tasks. The inspiration comes from a recent neuroscience study, which shows the human auditory system tends to perceive sound textures through time-averaged statistics. Based on these statistics, we further proposed to use linear discriminant analysis to eliminate redundancies among these statistics while keeping the discriminative information, providing an extreme com-pact representation for acoustic scenes. Experimental results show the outstanding performance of the proposed feature over the conventional handcrafted features.Comment: Accepted as a conference paper of Interspeech 201

Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events

In this paper, we propose a new strategy for acoustic scene classification (ASC) , namely recognizing acoustic scenes through identifying distinct sound events. This differs from existing strategies, which focus on characterizing global acoustical distributions of audio or the temporal evolution of short-term audio features, without analysis down to the level of sound events. To identify distinct sound events for each scene, we formulate ASC in a multi-instance learning (MIL) framework, where each audio recording is mapped into a bag-of-instances representation. Here, instances can be seen as high-level representations for sound events inside a scene. We also propose a MIL neural networks model, which implicitly identifies distinct instances (i.e., sound events). Furthermore, we propose two specially designed modules that model the multi-temporal scale and multi-modal natures of the sound events respectively. The experiments were conducted on the official development set of the DCASE2018 Task1 Subtask B, and our best-performing model improves over the official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy. This study indicates that recognizing acoustic scenes by identifying distinct sound events is effective and paves the way for future studies that combine this strategy with previous ones.Comment: code URL typo, code is available at https://github.com/hackerekcah/distinct-events-asc.gi

Melatonin Mitigates Salt Stress in Wheat Seedlings by Modulating Polyamine Metabolism

Melatonin, a small molecular weight indoleamine molecule, is involved in various biological processes and responses to environmental cues in plants. However, its function in abiotic stress response and the underlying mechanisms is less clear. In this study, we investigated the effect of melatonin on wheat seedlings growth under salt stress condition. Exogenous melatonin pretreatment partially mitigated the salt-induced inhibition of whole-plant growth as judged from shoot dry weight, IAA content, leaf photosynthesis rate, maximum photochemistry efficiency of photosystem II, and chlorophyll. The mitigation was also observed in reduced accumulation of H2O2 in melatonin-pretreated wheat seedlings exposed to salt stress. Exogenous melatonin increased endogenous melatonin content by evaluating the levels of TaSNAT transcript, which encodes a key regulatory enzyme in the melatonin biosynthetic pathway. Furthermore, melatonin increased polyamine contents by accelerating the metabolic flow from the precursor amino acids arginine and methionine to polyamines; melatonin also decreased the degradation of salt-induced polyamines. Taken together, these results provide the evidence that melatonin mitigates salt stress mainly through its regulation on polyamine metabolism of wheat seedlings

Medium Access Control for Opportunistic Concurrent Transmissions under Shadowing Channels

We study the problem of how to alleviate the exposed terminal effect in multi-hop wireless networks in the presence of log-normal shadowing channels. Assuming node location information, we propose an extension of the IEEE 802.11 MAC protocol that sched-ules concurrent transmissions in the presence of log-normal shadowing, thus mitigating the exposed terminal problem and improving network throughput and delay performance. We observe considerable improvements in throughput and delay achieved over the IEEE 802.11 MAC under various network topologies and channel conditions in ns-2 simulations, which justify the importance of considering channel randomness in MAC protocol design for multi-hop wireless networks