Price Convergence across Regions in India

The paper attempts to examine whether there is price convergence across various regions in India. Our results indicate significant presence of cross-sectional de- pendence in prices in India, rendering some of the standard panel unit root tests inapplicable. Using various panel unit root tests that are robust to cross-sectional dependence, it is found that relative price levels among various regions in India mean-revert. We decompose each series into a set of common factors and idiosyn- cratic components. The decomposition enables us to test stationarity and estimate half-lives of the common factors and the idiosyncratic components separately. Both these components in case of India are found to be stationary. Idiosyncratic price shocks, however, are found to be more persistent as compared to the common factor. The results also indicate that transportation costs proxied by distance can explain a part of the variation in prices between two locations in India.Cross co-integration, Cross-sectional dependence, Panel unit root tests, Common factor, Price convergence

Eventness: Object Detection on Spectrograms for Temporal Localization of Audio Events

In this paper, we introduce the concept of Eventness for audio event detection, which can, in part, be thought of as an analogue to Objectness from computer vision. The key observation behind the eventness concept is that audio events reveal themselves as 2-dimensional time-frequency patterns with specific textures and geometric structures in spectrograms. These time-frequency patterns can then be viewed analogously to objects occurring in natural images (with the exception that scaling and rotation invariance properties do not apply). With this key observation in mind, we pose the problem of detecting monophonic or polyphonic audio events as an equivalent visual object(s) detection problem under partial occlusion and clutter in spectrograms. We adapt a state-of-the-art visual object detection model to evaluate the audio event detection task on publicly available datasets. The proposed network has comparable results with a state-of-the-art baseline and is more robust on minority events. Provided large-scale datasets, we hope that our proposed conceptual model of eventness will be beneficial to the audio signal processing community towards improving performance of audio event detection.Comment: 5 pages, 3 figures, accepted to ICASSP 201