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There has been much recent interest in damped sinusoidal models, probably as a result of their relevance to magnetic resonance imaging. In \cite{about2010}, a model which allowed the sinusoid to decay to $0$ was examined, and a Fourier coefficient estimation procedure was proposed.

In a speech signal, Voice Onset Time (VOT) is the period between
the release of a plosive and the onset of vocal cord vibrations in the
production of the following sound. Voice Offset Time (VOFT), on
the other hand, is the period between the end of a voiced sound and
the release of the following plosive. Traditionally, VOT has been
studied across multiple disciplines and has been related to many
factors that influence human speech production, including physical,
physiological and psychological characteristics of the speaker. The

Consider a distributed estimation problem to be carried out by paid crowdworkers, where results are to be returned quickly and accurately. Estimation accuracy is a function of the number of workers completing the job and of the quality of the workers, both of which may be influenced by the payment offered. With limited budget, payment allocation should consider both effects to obtain best results. Since people are not deterministic, payment offers will lead to a random number of variable-quality workers, as governed by choice models.

Topological Data Analysis (TDA) is a topic which has recently seen many applications. The goal of this special session is to highlight the bridge between signal processing, machine learning and techniques in topological data analysis. In this way, we hope to encourage more engineers to start exploring TDA and its applications. This paper briefly introduces the standard techniques used in this area, delineates the common theme connecting the works presented in this session, and concludes with a brief summary of each of the papers presented.

In this paper, automatic speaker verification using normal and whispered speech is explored. Typically, for speaker verification systems with varying vocal effort inputs, standard solutions such as feature mapping or addition of data during parameter estimation (training) and enrollment stages result in a trade-off between accuracy gains with whispered test data and accuracy losses (up to 70% in equal error rate, EER) with normal test data. To overcome this shortcoming, this paper proposes two innovations.

Poster for ICASSP 2016