Underdetermined Blind Source Separation for Hard Clipped Stereophonic Mixtures Underdetermined source separation becomes more challenging when the observed mixtures are distorted. A novel framework for source separation from mixtures affected by clipping distortion is presented. Our method exploits knowledge about the geometry of clipped mixtures when the sources are disjoint in time. This knowledge constrains a convex optimisation problem, which we formulate and solve to recover the sources. This paper develops work previously published substantially by implementing novel preprocessing steps inspired by geometric observations of clipped mixtures of timedisjoint sources. Evaluation experiments showed a significant increase in objective recovery performance for the proposed method compared with sequential approaches for speech and synthetic sources.

Localization and classification of overlapping sound events based on spectrogramkeypoint using acousticsensornetwork data Automatic localization and classification of environmental sound events can provide great aid to many humancentric IoT applications. However as many papers have mentioned, environmental sound events in daily life are complicated and hard to classify especially when multiple sounds happen simultaneously. Unlike most works, which decompose and classify overlapping signals using a unified model, we first decompose overlapping sound signals with a spectrogramkeypoint based localization algorithm. These located and clustered spectrogramkeypoints are subsequently reused for sound source classification. Our major contribution is the modeling of a global cost function to synchronize the timedifferenceofarrivals (TDOA) of each small spectrogramkeypoint and further locating the sound sources by these clustered keypoints. With these clustered keypoints, 2 different classification models are used to classify the sound sources. Our experiments show that our solution is both accurate and lowcost in terms of calculation effort.