Multiscale Sequential Pattern Discovery and Alignment for LongDuration Waveform Similarity Quantification and Interpretation Longduration waveform similarity analysis identifies common waveform patterns among a group of signal recordings and uses these common waveform patterns to quantify the similarities of waveform signals. This quantification process of waveform similarity is an important analysis device for navigating, organizing, and interpreting longduration waveform recordings. This paper proposes a computational framework for quantifying the timeline similarity relationships employing the sequential pattern analysis of longduration recording signals at multiple analysis scales. Our proposed computational framework extends the scope of the conventional waveform similarity analysis toward more sophisticated patternmatching approaches and enables indepth explorations of larger datasets while allowing more quantitative interpretations and user interactions. The proposed similarity measurement framework is applied to two longduration recording datasets to demonstrate its usage scenarios.

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.