Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1941

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Browsing Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu by Publisher "Institute of Electrical and Electronics Engineers (IEEE)"

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    Article
    Citation - WoS: 22
    Audio Source Separation Using Variational Autoencoders and Weak Class Supervision
    (Institute of Electrical and Electronics Engineers (IEEE), 2019) Kırbız, Serap; Karamatlı, Ertuğ; Cemgil, Ali Taylan
    In this letter, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source constituting the mixture signal, we call this scenario as weak class supervision. We associate a variational autoencoder (VAE) with each source class within a non negative (compositional) model. Each VAE provides a prior model to identify the signal from its associated class in a sound mixture. After training the model on mixtures, we obtain a generative model for each source class and demonstrate our method on one-second mixtures of utterances of digits from 0 to 9. We show that the separation performance obtained by source class supervision is as good as the performance obtained by source signal supervision.
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    Citation - WoS: 10
    Citation - Scopus: 13
    Question Answering for Spoken Lecture Processing
    (Institute of Electrical and Electronics Engineers (IEEE), 2019) Ünlü, Merve; Saraçlar, Murat; Arısoy, Ebru
    This paper presents a question answering (QA) system developed for spoken lecture processing. The questions are presented to the system in written form and the answers are returned from lecture videos. In contrast to the widely studied reading comprehension style QA - the machine understands a passage of text and answers the questions related to that passage - our task introduces the challenge of searching the answers on longer text where the text corresponds to the erroneous transcripts of the lecture videos. Our initial experiments show that searching answers on longer text degrades the performance of the QA system drastically. Therefore, we propose splitting the transcriptions of lecture videos into short passages and determining passage-question matching using question aware passage representations. The proposed approach lets us utilize competitive neural network-based reading comprehension models for our task and improves the performance of the developed QA system