Leveraging Pretrained Image-text Models for Improving Audio-Visual Learning

Visually grounded speech systems learn from paired images and their spoken captions. Recently, there have been attempts to utilize the visually grounded models trained from images and their corresponding text captions, such as CLIP, to improve speech-based visually grounded models' performance. However, the majority of these models only utilize the pretrained image encoder. Cascaded SpeechCLIP attempted to generate localized word-level information and utilize both the pretrained image and text encoders. Despite using both, they noticed a substantial drop in retrieval performance. We proposed Segmental SpeechCLIP which used a hierarchical segmental speech encoder to generate sequences of word-like units. We used the pretrained CLIP text encoder on top of these word-like unit representations and showed significant improvements over the cascaded variant of SpeechCLIP. Segmental SpeechCLIP directly learns the word embeddings as input to the CLIP text encoder bypassing the vocabulary embeddings. Here, we explore mapping audio to CLIP vocabulary embeddings via regularization and quantization. As our objective is to distill semantic information into the speech encoders, we explore the usage of large unimodal pretrained language models as the text encoders. Our method enables us to bridge image and text encoders e.g. DINO and RoBERTa trained with uni-modal data. Finally, we extend our framework in audio-only settings where only pairs of semantically related audio are available. Experiments show that audio-only systems perform close to the audio-visual system

Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances

This paper explores grading text-based audio retrieval relevances with crowdsourcing assessments. Given a free-form text (e.g., a caption) as a query, crowdworkers are asked to grade audio clips using numeric scores (between 0 and 100) to indicate their judgements of how much the sound content of an audio clip matches the text, where 0 indicates no content match at all and 100 indicates perfect content match. We integrate the crowdsourced relevances into training and evaluating text-based audio retrieval systems, and evaluate the effect of using them together with binary relevances from audio captioning. Conventionally, these binary relevances are defined by captioning-based audio-caption pairs, where being positive indicates that the caption describes the paired audio, and being negative applies to all other pairs. Experimental results indicate that there is no clear benefit from incorporating crowdsourced relevances alongside binary relevances when the crowdsourced relevances are binarized for contrastive learning. Conversely, the results suggest that using only binary relevances defined by captioning-based audio-caption pairs is sufficient for contrastive learning.Comment: Accepted at DCASE 2023 Worksho

On Negative Sampling for Contrastive Audio-Text Retrieval

This paper investigates negative sampling for contrastive learning in the context of audio-text retrieval. The strategy for negative sampling refers to selecting negatives (either audio clips or textual descriptions) from a pool of candidates for a positive audio-text pair. We explore sampling strategies via model-estimated within-modality and cross-modality relevance scores for audio and text samples. With a constant training setting on the retrieval system from [1], we study eight sampling strategies, including hard and semi-hard negative sampling. Experimental results show that retrieval performance varies dramatically among different strategies. Particularly, by selecting semi-hard negatives with cross-modality scores, the retrieval system gains improved performance in both text-to-audio and audio-to-text retrieval. Besides, we show that feature collapse occurs while sampling hard negatives with cross-modality scores.Comment: Submitted to ICASSP202

Enhancing the Prediction of Emotional Experience in Movies using Deep Neural Networks: The Significance of Audio and Language

Our paper focuses on making use of deep neural network models to accurately predict the range of human emotions experienced during watching movies. In this certain setup, there exist three clear-cut input modalities that considerably influence the experienced emotions: visual cues derived from RGB video frames, auditory components encompassing sounds, speech, and music, and linguistic elements encompassing actors' dialogues. Emotions are commonly described using a two-factor model including valence (ranging from happy to sad) and arousal (indicating the intensity of the emotion). In this regard, a Plethora of works have presented a multitude of models aiming to predict valence and arousal from video content. However, non of these models contain all three modalities, with language being consistently eliminated across all of them. In this study, we comprehensively combine all modalities and conduct an analysis to ascertain the importance of each in predicting valence and arousal. Making use of pre-trained neural networks, we represent each input modality in our study. In order to process visual input, we employ pre-trained convolutional neural networks to recognize scenes[1], objects[2], and actions[3,4]. For audio processing, we utilize a specialized neural network designed for handling sound-related tasks, namely SoundNet[5]. Finally, Bidirectional Encoder Representations from Transformers (BERT) models are used to extract linguistic features[6] in our analysis. We report results on the COGNIMUSE dataset[7], where our proposed model outperforms the current state-of-the-art approaches. Surprisingly, our findings reveal that language significantly influences the experienced arousal, while sound emerges as the primary determinant for predicting valence. In contrast, the visual modality exhibits the least impact among all modalities in predicting emotions

Zero-shot audio captioning with audio-language model guidance and audio context keywords

Zero-shot audio captioning aims at automatically generating descriptive textual captions for audio content without prior training for this task. Different from speech recognition which translates audio content that contains spoken language into text, audio captioning is commonly concerned with ambient sounds, or sounds produced by a human performing an action. Inspired by zero-shot image captioning methods, we propose ZerAuCap, a novel framework for summarising such general audio signals in a text caption without requiring task-specific training. In particular, our framework exploits a pre-trained large language model (LLM) for generating the text which is guided by a pre-trained audio-language model to produce captions that describe the audio content. Additionally, we use audio context keywords that prompt the language model to generate text that is broadly relevant to sounds. Our proposed framework achieves state-of-the-art results in zero-shot audio captioning on the AudioCaps and Clotho datasets. Our code is available at https://github.com/ExplainableML/ZerAuCap.Comment: NeurIPS 2023 - Machine Learning for Audio Workshop (Oral

A sound approach: using large language models to generate audio descriptions for egocentric text-audio retrieval

Video databases from the internet are a valuable source of text-audio retrieval datasets. However, given that sound and vision streams represent different "views" of the data, treating visual descriptions as audio descriptions is far from optimal. Even if audio class labels are present, they commonly are not very detailed, making them unsuited for text-audio retrieval. To exploit relevant audio information from video-text datasets, we introduce a methodology for generating audio-centric descriptions using Large Language Models (LLMs). In this work, we consider the egocentric video setting and propose three new text-audio retrieval benchmarks based on the EpicMIR and EgoMCQ tasks, and on the EpicSounds dataset. Our approach for obtaining audio-centric descriptions gives significantly higher zero-shot performance than using the original visual-centric descriptions. Furthermore, we show that using the same prompts, we can successfully employ LLMs to improve the retrieval on EpicSounds, compared to using the original audio class labels of the dataset. Finally, we confirm that LLMs can be used to determine the difficulty of identifying the action associated with a sound

A Proposed System for Sound Retrieval Using MAS and ANN

As the use of sounds for computer interfaces, electronic equipment and multimedia contents, has increased, the role of sound design tools has become more important. In sound retrieval, picking one sound out from huge data is troublesome for users because of the difficulty of simultaneously listening to plural sounds and sometimes there are difficulties with speech and sound recognition. Consequently, an efficient retrieval method is required for sound databases. This research proposes a system aim to deal with sound retrieval in both two cases: authenticity and normal. In the first case, authenticity, two algorithms has been develop one for building the authentication database and the second deal with user sound sample to retrieve the matched authenticated samples. In the second case normal we develop algorithm to deal with user sound sample to retrieve all the matched samples. Many techniques used in this proposed system such as Artificial Neural Network (ANN), Data Encryption Standard (DES), Multi Agent System (MAS) and Fourier transformation (FT). Using these combinations of advanced and adaptive techniques supports the system to be reliable, secure and parallel