Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

Molecular Identification of Delphinids and Finless Porpoise (Cetacea) from the Arabian Sea and Bay of Bengal

The exact number of extant delphinid species from seas around India is still debated and the lack of adequate field keys and reliable inventory has resulted in misidentification of several species. As a part of a project to develop a molecular taxonomy of cetaceans from this region, partial sequences of mtDNA cytochrome b were generated from accidentally caught/stranded delphinids and finless porpoise. Species were identified by phylogenetic reconstruction of sample sequences with the reference sequences available in portals GenBank (NCBI) and the web-based program DNA Surveillance. A comparison was made with the homologous sequences of corresponding species from other seas of the world. Our molecular investigations allowed us to identify five species of cetaceans from Indian coasts, including Delphinus capensis, previously reported as D. delphis. We detected unique haplotypes in Indo pacific humpbacked dolphin (Sousa chinensis; n = 2) and finless porpoise (Neophocaena phocaenoides; n = 12) from Indian coast. On the other hand, some haplotypes were shared with other regional populations in spinner dolphin (Stenella longirostris; n = 16) and bottlenose dolphin (Tursiops aduncus; n = 3). Common dolphins (Delphinus capensis; n = 2) had both unique and shared haplotypes including one highly divergent sequence

A note on cetacean distribution in the Indian EEZ and contiguous seas during 2003-07

Relatively little is known about the distribution of cetaceans in Indian seas due to lack of systematic surveys. For collecting data on species distribution, 35 opportunistic surveys were conducted onboard FORV Sagar Sampada between October 2003 and February 2007 in the Indian EEZ and contiguous seas. In 5,254 hours of sighting effort, a total of 473 cetacean records were made with 5,865 individuals. The occurrence of 10 species from three cetacean families was confirmed. The Indo-Pacific bottlenose dolphin was the most frequently sighted species, whereas the spinner dolphin was dominant in terms of abundance. Long-beaked common dolphins, Indo-Pacific hump-backed dolphin and sperm whales were also recorded at frequent intervals. Cetaceans were found to have a wide geographical distribution in the Indian EEZ and contiguous seas. High abundance and species richness were recorded in the Southeastern Arabian Sea and southern Sri Lankan waters. From the information collected during the present study, the platform of opportunity has proved to be a useful means for cetacean surve

Indian Efforts on the Inventorization of Marine Mammal Species for their Conservation and Management

The present study is the first attempt to use molecular tools for identification of marine mammals in India. The objective was to develop a database of genetic sequences for future marine mammal research in addition to confirming the species identity of cetaceans and dugongs using a molecular approach. Partial sequencing of mitochondrial DNA loci was carried out in accidentally caught/stranded specimens of Spinner dolphin (Stenella longirostris), Pantropical spotted dolphin/bridled dolphin (Stenella attenuata), Bottlenose dolphin (Tursiops aduncus), Long-beaked common dolphin (Delphinus capensis), Indopacific humpbacked dolphin (Sousa chinensis), Risso’s dolphin (Grampus griseus), Finless porpoise (Neophocaena phocaenoides), Sperm whale (Physeter macrocephalus), Blue whale (Balaenoptera musculus), Bryde’s whale (Balaenoptera edeni) and Dugong (Dugong dugon). Molecular identification of species was done by phylogenetic reconstruction of the sequences using portals GenBank and DNA Surveillance. Apart from ratifying their morphological identification, the analysis was able to distinguish specimens that otherwise, could not have been identified using conventional approaches. Phylogenetic analysis of the Sousa-Stenella-Tursiops-Delphinus group indicated more or less robust monophyly for all species in this complex, except Delphinus capensis. A sister-group relationship for Sperm whales and Baleen whales was evident, that would place the former closer to the latter than to any other group of toothed whales

Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet

The problem of distinguishing natural images from photo-realistic computer-generated ones either addresses natural images versus computer graphics or natural images versus GAN images, at a time. But in a real-world image forensic scenario, it is highly essential to consider all categories of image generation, since in most cases image generation is unknown. We, for the first time, to our best knowledge, approach the problem of distinguishing natural images from photo-realistic computer-generated images as a three-class classification task classifying natural, computer graphics, and GAN images. For the task, we propose a Multi-Colorspace fused EfficientNet model by parallelly fusing three EfficientNet networks that follow transfer learning methodology where each network operates in different colorspaces, RGB, LCH, and HSV, chosen after analyzing the efficacy of various colorspace transformations in this image forensics problem. Our model outperforms the baselines in terms of accuracy, robustness towards post-processing, and generalizability towards other datasets. We conduct psychophysics experiments to understand how accurately humans can distinguish natural, computer graphics, and GAN images where we could observe that humans find difficulty in classifying these images, particularly the computer-generated images, indicating the necessity of computational algorithms for the task. We also analyze the behavior of our model through visual explanations to understand salient regions that contribute to the model's decision making and compare with manual explanations provided by human participants in the form of region markings, where we could observe similarities in both the explanations indicating the powerful nature of our model to take the decisions meaningfully.Comment: 13 page

Observations on incidental catch of cetaceans in three landing centres along the Indian coast

A short term survey to quantify the number of marine mammals incidentally caught, and interviews to gain perceptions of local fishers towards issues of by-catch, were conducted. A total of 44 cetaceans were recorded as incidental catches at Chennai, Kakinada and Mangalore fishing harbours during 80 days of observation. Six species of dolphins and one species of porpoise were recorded. The spinner dolphin Stenella longirostris was the most frequently caught (38.6%), followed by the finless porpoise Neophocaena phocaenoides (31.8%). Gillnets and purse seines operated from motorised boats accounted for the entire by-catch. It is estimated that 9000–10,000 cetaceans are killed by gillnets every year along the Indian coast. The intricacies and possibilities of reducing cetacean kills by gillnets are discussed in the pape

Stomach contents of cetaceans incidentally caught along Mangalore and Chennai coasts of India

Abstract The stomachs of 32 individuals of seven cetacean species incidentally caught in gill net and purseseine fisheries along Mangalore and Chennai coasts (India) between 2004 and 2006 were examined. The whole stomach (fore-gut, mid-gut and hind-gut) was examined in all cases. Prey remains (666 prey items comprising six species of teleosts, one crustacean and one squid species) were found in the stomachs of eight individuals (the remaining 24 stomachs were found to be empty). All cetaceans were found to feed mostly on teleosts with wide range of trophic levels. Based on an index that included frequency of occurrence, percentage by number and by weight, the oil sardine Sardinella longiceps was the main prey in the sample. Cetaceans appear to favour both pelagic as well as demersal prey, possibly indicating surface and benthic feeding habits