Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Machine Learning-based Silence Detection in Call Center Telephone Conversations
Date
2019-01-01
Author
Iheme, Leonardo O.
Ozan, Sukru
Akagündüz, Erdem
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
48
views
0
downloads
Cite This
This study presents the development of a voice activity detection (VAD) system tested on call center telephony data obtained from our local site. The concept of bag of audio words (BoAW) combined with a naive Bayes classifier was applied to achieve the task. It was formulated as a binary classification problem with speech as the positive class and silence/background noise as the negative class. All the processing was performed on the Mel-frequency cepstral coefficients (MFCCs) extracted from the audio recordings. The results which are presented as accuracy score and receiver operating characteristics (ROC) indicate an excellent performance of the developed model. The system is to be deployed within our call center to aid data analysis and improve overall efficiency of the center.
URI
https://hdl.handle.net/11511/93724
DOI
https://doi.org/10.1109/idap.2019.8875958
Conference Name
International Conference on Artificial Intelligence and Data Processing (IDAP)
Collections
Graduate School of Informatics, Conference / Seminar
Suggestions
OpenMETU
Core
Efficient and Reliable Multicast of Data in APCO P25 Systems
Cigirkan, Gulay; Girici, Tolga; Yüksel Turgut, Ayşe Melda (2015-01-01)
In this paper, we investigate an efficient scheme for data multicasting in narrowband public safety radio systems. The proposed scheme uses fountain encoding, in order to avoid feedback messages for each individual packet. We first propose a multistage estimation scheme that is based on slotted random access and that does not require any prior knowledge of number of users. The rest of the proposed scheme consists of iterative transmission and feedback/estimation phases. In feedback phases, the base station ...
Fuzzy Semantic Web Architecture for Activity Detection in Wireless Multimedia Sensor Network Applications
Ozdin, Ali Nail; Yazıcı, Adnan; KOYUNCU, Murat (2019-01-01)
This study aims to increase the reliability of activity detection in Wireless Multimedia Sensor Networks (WMSNs) by using Semantic Web technologies extended with fuzzy logic. The proposed approach consists of three layers: the sensor layer, the data layer, and the Semantic Web layer. The sensor layer comprises a WMSN comprising sensor nodes with multimedia and scalar sensors. The data layer retrieves and stores data from the sink of WMSN. At the top of the architecture, there is a semantic web layer that in...
Multi-target tracking with PHD filter using Doppler-only measurements
Guldogan, Mehmet B.; Lindgren, David; Gustafsson, Fredrik; Habberstad, Hans; Orguner, Umut (2014-04-01)
In this paper, we address the problem of multi-target detection and tracking over a network of separately located Doppler-shift measuring sensors. For this challenging problem, we propose to use the probability hypothesis density (PHD) filter and present two implementations of the PHD filter, namely the sequential Monte Carlo PHD (SMC-PHD) and the Gaussian mixture PHD (GM-PHD) filters. Performances of both filters are carefully studied and compared for the considered challenging tracking problem. Simulation...
Multimodal Wireless Sensor Network-Based Ambient Assisted Living in Real Homes with Multiple Residents
Tunca, Can; Alemdar, Hande; Ertan, Halil; Incel, Ozlem Durmaz; Ersoy, Cem (MDPI AG, 2014-06-01)
Human activity recognition and behavior monitoring in a home setting using wireless sensor networks (WSNs) provide a great potential for ambient assisted living (AAL) applications, ranging from health and wellbeing monitoring to resource consumption monitoring. However, due to the limitations of the sensor devices, challenges in wireless communication and the challenges in processing large amounts of sensor data in order to recognize complex human activities, WSN-based AAL systems are not effectively integr...
Optimal wavelength combinations for near-infrared spectroscopic monitoring of changes in brain tissue hemoglobin and cytochrome c oxidase concentrations
Arifler, Dizem; Zhu, Tingting; Madaan, Sara; Tachtsidis, Ilias (The Optical Society, 2015-2-23)
We analyze broadband near-infrared spectroscopic measurements obtained from newborn piglets subjected to hypoxia-ischemia and we aim to identify optimal wavelength combinations for monitoring cerebral tissue chromophores. We implement an optimization routine based on the genetic algorithm to perform a heuristic search for discrete wavelength combinations that can provide accurate concentration information when benchmarked against the gold standard of 121 wavelengths. The results indicate that it is possible...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
L. O. Iheme, S. Ozan, and E. Akagündüz, “Machine Learning-based Silence Detection in Call Center Telephone Conversations,” presented at the International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Türkiye, 2019, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93724.
