22,307 research outputs found
Deep Recurrent Survival Analysis
Survival analysis is a hotspot in statistical research for modeling
time-to-event information with data censorship handling, which has been widely
used in many applications such as clinical research, information system and
other fields with survivorship bias. Many works have been proposed for survival
analysis ranging from traditional statistic methods to machine learning models.
However, the existing methodologies either utilize counting-based statistics on
the segmented data, or have a pre-assumption on the event probability
distribution w.r.t. time. Moreover, few works consider sequential patterns
within the feature space. In this paper, we propose a Deep Recurrent Survival
Analysis model which combines deep learning for conditional probability
prediction at fine-grained level of the data, and survival analysis for
tackling the censorship. By capturing the time dependency through modeling the
conditional probability of the event for each sample, our method predicts the
likelihood of the true event occurrence and estimates the survival rate over
time, i.e., the probability of the non-occurrence of the event, for the
censored data. Meanwhile, without assuming any specific form of the event
probability distribution, our model shows great advantages over the previous
works on fitting various sophisticated data distributions. In the experiments
on the three real-world tasks from different fields, our model significantly
outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code:
https://github.com/rk2900/drs
Speech-Based Blood Pressure Estimation with Enhanced Optimization and Incremental Clustering
Blood Pressure (BP) estimation plays a pivotal role in diagnosing various
health conditions, highlighting the need for innovative approaches to overcome
conventional measurement challenges. Leveraging machine learning and speech
signals, this study investigates accurate BP estimation with a focus on
preprocessing, feature extraction, and real-time applications. An advanced
clustering-based strategy, incorporating the k-means algorithm and the proposed
Fact-Finding Instructor optimization algorithm, is introduced to enhance
accuracy. The combined outcome of these clustering techniques enables robust BP
estimation. Moreover, extending beyond these insights, this study delves into
the dynamic realm of contemporary digital content consumption. Platforms like
YouTube have emerged as influential spaces, presenting an array of videos that
evoke diverse emotions. From heartwarming and amusing content to intense
narratives, YouTube captures a spectrum of human experiences, influencing
information access and emotional engagement. Within this context, this research
investigates the interplay between YouTube videos and physiological responses,
particularly Blood Pressure (BP) levels. By integrating advanced BP estimation
techniques with the emotional dimensions of YouTube videos, this study enriches
our understanding of how modern media environments intersect with health
implications.Comment: 29 pages, 2 tables, 9 figure
Singing voice correction using canonical time warping
Expressive singing voice correction is an appealing but challenging problem.
A robust time-warping algorithm which synchronizes two singing recordings can
provide a promising solution. We thereby propose to address the problem by
canonical time warping (CTW) which aligns amateur singing recordings to
professional ones. A new pitch contour is generated given the alignment
information, and a pitch-corrected singing is synthesized back through the
vocoder. The objective evaluation shows that CTW is robust against
pitch-shifting and time-stretching effects, and the subjective test
demonstrates that CTW prevails the other methods including DTW and the
commercial auto-tuning software. Finally, we demonstrate the applicability of
the proposed method in a practical, real-world scenario
Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays
In the analysis of acoustic scenes, often the occurring sounds have to be
detected in time, recognized, and localized in space. Usually, each of these
tasks is done separately. In this paper, a model-based approach to jointly
carry them out for the case of multiple simultaneous sources is presented and
tested. The recognized event classes and their respective room positions are
obtained with a single system that maximizes the combination of a large set of
scores, each one resulting from a different acoustic event model and a
different beamformer output signal, which comes from one of several
arbitrarily-located small microphone arrays. By using a two-step method, the
experimental work for a specific scenario consisting of meeting-room acoustic
events, either isolated or overlapped with speech, is reported. Tests carried
out with two datasets show the advantage of the proposed approach with respect
to some usual techniques, and that the inclusion of estimated priors brings a
further performance improvement.Comment: Computational acoustic scene analysis, microphone array signal
processing, acoustic event detectio
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