Automated Audio Captioning using Audio Event Clues

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.

Audio Captioning Using Sound Event Detection

This technical report proposes an audio captioning system for DCASE 2021 Task 6 audio captioning challenge. Our proposed model is based on an encoder-decoder architecture with bi-directional Gated Recurrent Units (BiGRU) using pretrained audio features and sound event detection. A pretrained neural network (PANN) is used to extract audio features and Word2Vec is selected with the aim of extracting word embeddings from the audio captions. To create semantically meaningful captions, we extract sound events from the audio clips and feed the encoder-decoder architecture with sound events in addition to PANNs features. Our experiments on the Clotho dataset show that our proposed method significantly achieves better results than the challenge baseline model across all evaluation metrics.

Audio Captioning with Composition of Acoustic and Semantic Information

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 efficiency 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. Keywords: Audio captioning; PANNs; VGGish; GRU; BiGRU.

Audio Captioning using Gated Recurrent Units

Audio captioning is a recently proposed task for automatically generating a textual description of a given audio clip. In this study, a novel deep network architecture with audio embeddings is presented to predict audio captions. Within the aim of extracting audio features in addition to log Mel energies, VGGish audio embedding model is used to explore the usability of audio embeddings in the audio captioning task. The proposed architecture encodes audio and text input modalities separately and combines them before the decoding stage. Audio encoding is conducted through Bi-directional Gated Recurrent Unit (BiGRU) while GRU is used for the text encoding phase. Following this, we evaluate our model by means of the newly published audio captioning performance dataset, namely Clotho, to compare the experimental results with the literature. Our experimental results show that the proposed BiGRU-based deep model outperforms the state of the art results.

Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification

Deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition. In this study, we investigate various aspects of convolutional neural networks (CNNs) from the big data perspective. We analyze recent studies and different network architectures both in terms of running time and accuracy. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the art results on benchmark datasets while keeping computational costs at a lower level. Another contribution of our paper is that these state-of-the-art results can be reached without using extensive data augmentation techniques.