2022/04/18 by Ayşegül Özkaya Eren, Eren, Ayşegül Özkaya, Mustafa Sert +1
Arts and Humanities · Computer Science · #Audio and Speech Processing (eess.AS) #Diverse Musicological Studies #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.08567
openalex publication_date 2022/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Audio captioning is an important research area that aims to generate meaningful descriptions for audio clips. Most of the existing research extracts acoustic features of audio clips as input to encoder-decoder and transformer architectures to produce the captions in a sequence-to-sequence manner. Due to data insufficiency and the architecture's inadequate learning capacity, additional information is needed to generate natural language sentences, as well as acoustic features. To address these problems, an encoder-decoder architecture is proposed that learns from both acoustic features and extracted audio event labels as inputs. The proposed model is based on pre-trained acoustic features and audio event detection. Various experiments used different acoustic features, word embedding models, audio event label extraction methods, and implementation configurations to show which combinations have better performance on the audio captioning task. Results of the extensive experiments on multiple datasets show that using audio event labels with the acoustic features improves the recognition performance and the proposed method either outperforms or achieves competitive results with the state-of-the-art models.