TASLP Volume 29 | 2021

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January, 2021

TASLP Volume 29 | 2021

Geometry calibration is an inherent challenge in distributed acoustic sensor networks. To mitigate this problem, a passive geometry calibration approach based on distributed damped Newton optimization is proposed. Specifically, a geometric cost function incorporating direction of arrivals (DoAs) and time difference of arrivals (TDoAs) is first formulated, and then its identifiability conditions are given.

Speaker diarization is an important problem that is topical, and is especially useful as a preprocessor for conversational speech related applications. The objective of this article is two-fold: (i) segment initialization by uniformly distributing speaker information across the initial segments, and (ii) incorporating speaker discriminative features within the unsupervised diarization framework. In the first part of the work, a varying length segment initialization technique for Information Bottleneck (IB) based speaker diarization system using phoneme rate as the side information is proposed. This initialization distributes speaker information uniformly across the segments and provides a better starting point for IB based clustering. 

One practical requirement of the music copyright management is the estimation of music relative loudness, which is mostly ignored in existing music detection works. To solve this problem, we study the joint task of music detection and music relative loudness estimation. To be specific, we observe that the joint task has two characteristics, i.e., temporality and hierarchy, which could facilitate to obtain the solution. For example, a tiny fragment of audio is temporally related to its neighbor fragments because they may all belong to the same event, and the event classes of the fragment in the two tasks have a hierarchical relationship. Based on the above observation, we reformulate the joint task as hierarchical event detection and localization problem. To solve this problem, we further propose Hierarchical Regulated Iterative Networks (HRIN), which includes two variants, termed as HRIN-r and HRIN-cr, which are based on recurrent and convolutional recurrent modules. 

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