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https://arxiv.org/pdf/1009.4972.pdf
identify and verify human speech more accurately. This work presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM). Mel-frequency cepstrum coefficients (MFCCs) and their statistical distribution properties are used as features, which will be inputs to the neural network [8]. 2. Voice processing
https://pdfs.semanticscholar.org/82f3/0ec579faf5eee57aef683f672636447192ec.pdf
Feature extraction, vector quantization, MFCC, SVM high inter 1. INTRODUCTION Speaker identification is the process of automatically identifying a speaker by machine using some characteristics of speaker’s voice [1]. Speaker recognition can be categorized into identification and verification [2]. Speaker identification is the process of
https://cardiacalert.cs.washington.edu/
An under-appreciated diagnostic element of cardiac arrest is the presence of agonal breathing, an audible biomarker and brainstem reflex that arises in the setting of severe hypoxia. Here, we demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a …
https://en.wikipedia.org/wiki/Support-vector_machine
Support-vector machine weights have also been used to interpret SVM models in the past. Posthoc interpretation of support-vector machine models in order to identify features used by the model to make predictions is a relatively new area of research with special significance in the biological sciences. History
https://arxiv.org/pdf/1810.04826.pdf
First, identifying the number of speakers N in the recording, which in realistic scenarios is unknown. Secondly, ... the embedding vector of the target speaker, pre-viously computed with the speaker encoder; and (2) the noisy ... entropy classification network, our speaker encoder network is
https://link.springer.com/chapter/10.1007/978-3-030-34387-3_1
In this paper, a novel approach for the task of voiceprint recognition was proposed. The combination of deep belief network (DBN) and support vector machine (SVM) was used to identify the voiceprint of 10 different individuals.
https://www.sciencedirect.com/science/article/pii/S0305048301000263
Application of support vector machines in financial time series forecasting. ... This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. ... Schmidt M. Identifying speaker with support vector networks. Interface ’96 Proceedings, Sydney, 1996.
https://link.springer.com/article/10.1023/A:1015249103876
ε-Descending Support Vector Machines for Financial Time Series Forecasting. Authors; ... Identifying speaker with support vector networks, In Interface '96 Proceedings, Sydney, 1996. ... In Proceedings of International Conference on Artificial Neural Networks, pp. 999, 1997.Cited by: 13
https://rd.springer.com/article/10.1023%2FA%3A1009715923555
Jun 01, 1998 · The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global.Cited by: 21704
https://towardsdatascience.com/intro-to-deep-learning-c025efd92535
Jan 13, 2019 · We live in a world where, for better and for worse, we are constantly surrounded by deep learning algorithms. From social network filtering to driverless cars to movie recommendations, and from financial fraud detection to drug discovery to medical image processing (…is that bump cancer?), the field of deep learning influences our lives and our decisions every single day.
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