Browsing by Author "Eren, Aysegul Ozkaya"

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    Audio Captioning Based on Combined Audio and Semantic Embeddings
    (2020) Eren, Aysegul Ozkaya; Sert, Mustafa
    Audio captioning is a recently proposed task for automatically generating a textual description of a given audio clip. Most existing approaches use the encoder-decoder model without using semantic information. In this study, we propose a bi-directional Gated Recurrent Unit (BiGRU) model based on encoder-decoder architecture using audio and semantic embeddings. To obtain semantic embeddings, we extract subject-verb embeddings using the subjects and verbs from the audio captions. We use a Multilayer Perceptron classifier to predict subject-verb embeddings of test audio clips for the testing stage. Within the aim of extracting audio features, in addition to log Mel energies, we use a pretrained audio neural network (PANN) as a feature extractor which is used for the first time in the audio captioning task to explore the usability of audio embeddings in the audio captioning task. We combine audio embeddings and semantic embeddings to feed the BiGRU-based encoder-decoder model. Following this, we evaluate our model on two audio captioning datasets: Clotho and AudioCaps. Experimental results show that the proposed BiGRU-based deep model significantly outperforms the state of the art results across different evaluation metrics and inclusion of semantic information enhance the captioning performance.
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    Audio Captioning with Composition of Acoustic and Semantic Information
    (2021) Eren, Aysegul Ozkaya; Sert, Mustafa
    Generating audio captions is a new research area that combines audio and natural language processing to create meaningful textual descriptions for audio clips. To address this problem, previous studies mostly use the encoder-decoder-based models without considering semantic information. To fill this gap, we present a novel encoder-decoder architecture using bi-directional Gated Recurrent Units (BiGRU) with audio and semantic embeddings. We extract semantic embedding by obtaining subjects and verbs from the audio clip captions and combine these embedding with audio embedding to feed the BiGRU-based encoder-decoder model. To enable semantic embeddings for the test audios, we introduce a Multilayer Perceptron classifier to predict the semantic embeddings of those clips. We also present exhaustive experiments to show the effciency of different features and datasets for our proposed model the audio captioning task. To extract audio features, we use the log Mel energy features, VGGish embeddings, and a pretrained audio neural network (PANN) embeddings. Extensive experiments on two audio captioning datasets Clotho and AudioCaps show that our proposed model outperforms state-of-the-art audio captioning models across different evaluation metrics and using the semantic information improves the captioning performance.